diff --git a/funasr/bin/asr_infer.py b/funasr/bin/asr_infer.py
index a537a73fe..0ce8dd849 100644
--- a/funasr/bin/asr_infer.py
+++ b/funasr/bin/asr_infer.py
@@ -285,6 +285,7 @@ class Speech2TextParaformer:
             nbest: int = 1,
             frontend_conf: dict = None,
             hotword_list_or_file: str = None,
+            clas_scale: float = 1.0,
             decoding_ind: int = 0,
             **kwargs,
     ):
@@ -382,6 +383,7 @@ class Speech2TextParaformer:
         # 6. [Optional] Build hotword list from str, local file or url
         self.hotword_list = None
         self.hotword_list = self.generate_hotwords_list(hotword_list_or_file)
+        self.clas_scale = clas_scale
 
         is_use_lm = lm_weight != 0.0 and lm_file is not None
         if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
@@ -446,16 +448,20 @@ class Speech2TextParaformer:
         pre_token_length = pre_token_length.round().long()
         if torch.max(pre_token_length) < 1:
             return []
-        if not isinstance(self.asr_model, ContextualParaformer) and not isinstance(self.asr_model,
-                                                                                   NeatContextualParaformer):
+        if not isinstance(self.asr_model, ContextualParaformer) and \
+            not isinstance(self.asr_model, NeatContextualParaformer):
             if self.hotword_list:
                 logging.warning("Hotword is given but asr model is not a ContextualParaformer.")
             decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds,
                                                                      pre_token_length)
             decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
         else:
-            decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds,
-                                                                     pre_token_length, hw_list=self.hotword_list)
+            decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, 
+                                                                     enc_len, 
+                                                                     pre_acoustic_embeds,
+                                                                     pre_token_length, 
+                                                                     hw_list=self.hotword_list,
+                                                                     clas_scale=self.clas_scale)
             decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
 
         if isinstance(self.asr_model, BiCifParaformer):
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index 5d1b80488..026874e28 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -260,6 +260,7 @@ def inference_paraformer(
         export_mode = param_dict.get("export_mode", False)
     else:
         hotword_list_or_file = None
+    clas_scale = param_dict.get('clas_scale', 1.0)
 
     if kwargs.get("device", None) == "cpu":
         ngpu = 0
@@ -292,6 +293,7 @@ def inference_paraformer(
         penalty=penalty,
         nbest=nbest,
         hotword_list_or_file=hotword_list_or_file,
+        clas_sacle=clas_scale,
     )
 
     speech2text = Speech2TextParaformer(**speech2text_kwargs)
diff --git a/funasr/models/decoder/contextual_decoder.py b/funasr/models/decoder/contextual_decoder.py
index 78105ab31..18d486136 100644
--- a/funasr/models/decoder/contextual_decoder.py
+++ b/funasr/models/decoder/contextual_decoder.py
@@ -246,6 +246,7 @@ class ContextualParaformerDecoder(ParaformerSANMDecoder):
         ys_in_pad: torch.Tensor,
         ys_in_lens: torch.Tensor,
         contextual_info: torch.Tensor,
+        clas_scale: float = 1.0,
         return_hidden: bool = False,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         """Forward decoder.
@@ -285,7 +286,7 @@ class ContextualParaformerDecoder(ParaformerSANMDecoder):
         cx, tgt_mask, _, _, _ = self.bias_decoder(x_self_attn, tgt_mask, contextual_info, memory_mask=contextual_mask)
 
         if self.bias_output is not None:
-            x = torch.cat([x_src_attn, cx], dim=2)
+            x = torch.cat([x_src_attn, cx*clas_scale], dim=2)
             x = self.bias_output(x.transpose(1, 2)).transpose(1, 2)  # 2D -> D
             x = x_self_attn + self.dropout(x)
 
diff --git a/funasr/models/e2e_asr_contextual_paraformer.py b/funasr/models/e2e_asr_contextual_paraformer.py
index cfb500815..b8ae951fc 100644
--- a/funasr/models/e2e_asr_contextual_paraformer.py
+++ b/funasr/models/e2e_asr_contextual_paraformer.py
@@ -343,7 +343,7 @@ class NeatContextualParaformer(Paraformer):
             input_mask_expand_dim, 0)
         return sematic_embeds * tgt_mask, decoder_out * tgt_mask
 
-    def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None):
+    def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None, clas_scale=1.0):
         if hw_list is None:
             hw_list = [torch.Tensor([1]).long().to(encoder_out.device)]  # empty hotword list
             hw_list_pad = pad_list(hw_list, 0)
@@ -365,7 +365,7 @@ class NeatContextualParaformer(Paraformer):
             hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
         
         decoder_outs = self.decoder(
-            encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed
+            encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed, clas_scale=clas_scale
         )
         decoder_out = decoder_outs[0]
         decoder_out = torch.log_softmax(decoder_out, dim=-1)