modelscope-funasr热词激励的模型训练阶段的任务会不会导致语音字数预测受到影响？

modelscope-funasr热词激励的模型训练阶段的任务会不会导致语音字数预测受到影响？ def calc_predictor(self, encoder_out, encoder_out_lens):
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
)
return predictor_outs[:4]

def _calc_seaco_loss(
    self,
    encoder_out: torch.Tensor,
    encoder_out_lens: torch.Tensor,
    ys_pad: torch.Tensor,
    ys_lengths: torch.Tensor,
    hotword_pad: torch.Tensor,
    hotword_lengths: 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(encoder_out.device)
    pre_acoustic_embeds = self.predictor(
        encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id
    )[0]
    # decoder forward
    decoder_out, _ = self.decoder(
        encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_lengths, return_hidden=True
    )
    selected = self._hotword_representation(hotword_pad, hotword_lengths)
    contextual_info = (
        selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
    )
    num_hot_word = contextual_info.shape[1]
    _contextual_length = (
        torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
    )
    # dha core
    cif_attended, _ = self.seaco_decoder(
        contextual_info, _contextual_length, pre_acoustic_embeds, ys_lengths
    )
    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