diff --git a/examples/industrial_data_pretraining/lcbnet/demo2.sh b/examples/industrial_data_pretraining/lcbnet/demo2.sh
index cfb5b235e..0d5a4f031 100755
--- a/examples/industrial_data_pretraining/lcbnet/demo2.sh
+++ b/examples/industrial_data_pretraining/lcbnet/demo2.sh
@@ -6,7 +6,6 @@ python -m funasr.bin.inference \
 --config-name="config.yaml" \
 ++init_param=${file_dir}/model.pb \
 ++tokenizer_conf.token_list=${file_dir}/tokens.txt \
-++frontend_conf.cmvn_file=${file_dir}/am.mvn \
 ++input=[${file_dir}/wav.scp,${file_dir}/ocr_text] \
 +data_type='["kaldi_ark", "text"]' \
 ++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
diff --git a/funasr/models/lcbnet/model.py b/funasr/models/lcbnet/model.py
index 1acce785f..f45e71d6f 100644
--- a/funasr/models/lcbnet/model.py
+++ b/funasr/models/lcbnet/model.py
@@ -21,6 +21,7 @@ from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
 from funasr.utils import postprocess_utils
 from funasr.utils.datadir_writer import DatadirWriter
 from funasr.register import tables
+
 import pdb
 @tables.register("model_classes", "LCBNet")
 class LCBNet(nn.Module):
@@ -92,6 +93,7 @@ class LCBNet(nn.Module):
         bias_predictor_class = tables.encoder_classes.get(bias_predictor)
         bias_predictor = bias_predictor_class(**bias_predictor_conf)
 
+
         if decoder is not None:
             decoder_class = tables.decoder_classes.get(decoder)
             decoder = decoder_class(
@@ -272,15 +274,15 @@ class LCBNet(nn.Module):
                 ind: int
         """
         with autocast(False):
-
+            pdb.set_trace()
             # Data augmentation
             if self.specaug is not None and self.training:
                 speech, speech_lengths = self.specaug(speech, speech_lengths)
-            
+            pdb.set_trace()
             # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
             if self.normalize is not None:
                 speech, speech_lengths = self.normalize(speech, speech_lengths)
-        
+        pdb.set_trace()
         # Forward encoder
         # feats: (Batch, Length, Dim)
         # -> encoder_out: (Batch, Length2, Dim2)
@@ -297,7 +299,7 @@ class LCBNet(nn.Module):
         
         if intermediate_outs is not None:
             return (encoder_out, intermediate_outs), encoder_out_lens
-        
+        pdb.set_trace()
         return encoder_out, encoder_out_lens
     
     def _calc_att_loss(
@@ -442,6 +444,7 @@ class LCBNet(nn.Module):
 
         speech = speech.to(device=kwargs["device"])
         speech_lengths = speech_lengths.to(device=kwargs["device"])
+        pdb.set_trace()
         # Encoder
         encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
         if isinstance(encoder_out, tuple):
diff --git a/funasr/utils/load_utils.py b/funasr/utils/load_utils.py
index 963f5c258..644af2324 100644
--- a/funasr/utils/load_utils.py
+++ b/funasr/utils/load_utils.py
@@ -108,10 +108,7 @@ def extract_fbank(data, data_len = None, data_type: str="sound", frontend=None,
             data_list.append(data_i)
             data_len.append(data_i.shape[0])
         data = pad_sequence(data_list, batch_first=True) # data: [batch, N]
-    # import pdb;
-    # pdb.set_trace()
-    # if data_type == "sound":
-    pdb.set_trace()
+
     data, data_len = frontend(data, data_len, **kwargs)
     
     if isinstance(data_len, (list, tuple)):