diff --git a/funasr/models/e2e_asr_paraformer.py b/funasr/models/e2e_asr_paraformer.py
index 605a95c8a..f414e4fd0 100644
--- a/funasr/models/e2e_asr_paraformer.py
+++ b/funasr/models/e2e_asr_paraformer.py
@@ -92,17 +92,8 @@ class Paraformer(FunASRModel):
         self.frontend = frontend
         self.specaug = specaug
         self.normalize = normalize
-        self.preencoder = preencoder
-        self.postencoder = postencoder
         self.encoder = encoder
 
-        if not hasattr(self.encoder, "interctc_use_conditioning"):
-            self.encoder.interctc_use_conditioning = False
-        if self.encoder.interctc_use_conditioning:
-            self.encoder.conditioning_layer = torch.nn.Linear(
-                vocab_size, self.encoder.output_size()
-            )
-
         self.error_calculator = None
 
         if ctc_weight == 1.0:
@@ -170,9 +161,7 @@ class Paraformer(FunASRModel):
 
         # 1. Encoder
         encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
-        intermediate_outs = None
         if isinstance(encoder_out, tuple):
-            intermediate_outs = encoder_out[1]
             encoder_out = encoder_out[0]
 
         loss_att, acc_att, cer_att, wer_att = None, None, None, None
@@ -190,30 +179,6 @@ class Paraformer(FunASRModel):
             stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
             stats["cer_ctc"] = cer_ctc
 
-        # Intermediate CTC (optional)
-        loss_interctc = 0.0
-        if self.interctc_weight != 0.0 and intermediate_outs is not None:
-            for layer_idx, intermediate_out in intermediate_outs:
-                # we assume intermediate_out has the same length & padding
-                # as those of encoder_out
-                loss_ic, cer_ic = self._calc_ctc_loss(
-                    intermediate_out, encoder_out_lens, text, text_lengths
-                )
-                loss_interctc = loss_interctc + loss_ic
-
-                # Collect Intermedaite CTC stats
-                stats["loss_interctc_layer{}".format(layer_idx)] = (
-                    loss_ic.detach() if loss_ic is not None else None
-                )
-                stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
-
-            loss_interctc = loss_interctc / len(intermediate_outs)
-
-            # calculate whole encoder loss
-            loss_ctc = (
-                               1 - self.interctc_weight
-                       ) * loss_ctc + self.interctc_weight * loss_interctc
-
         # 2b. Attention decoder branch
         if self.ctc_weight != 1.0:
             loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_loss(
@@ -281,29 +246,8 @@ class Paraformer(FunASRModel):
             if self.normalize is not None:
                 feats, feats_lengths = self.normalize(feats, feats_lengths)
 
-        # Pre-encoder, e.g. used for raw input data
-        if self.preencoder is not None:
-            feats, feats_lengths = self.preencoder(feats, feats_lengths)
-
         # 4. Forward encoder
-        # feats: (Batch, Length, Dim)
-        # -> encoder_out: (Batch, Length2, Dim2)
-        if self.encoder.interctc_use_conditioning:
-            encoder_out, encoder_out_lens, _ = self.encoder(
-                feats, feats_lengths, ctc=self.ctc
-            )
-        else:
-            encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
-        intermediate_outs = None
-        if isinstance(encoder_out, tuple):
-            intermediate_outs = encoder_out[1]
-            encoder_out = encoder_out[0]
-
-        # Post-encoder, e.g. NLU
-        if self.postencoder is not None:
-            encoder_out, encoder_out_lens = self.postencoder(
-                encoder_out, encoder_out_lens
-            )
+        encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
 
         assert encoder_out.size(0) == speech.size(0), (
             encoder_out.size(),
@@ -314,9 +258,6 @@ class Paraformer(FunASRModel):
             encoder_out_lens.max(),
         )
 
-        if intermediate_outs is not None:
-            return (encoder_out, intermediate_outs), encoder_out_lens
-
         return encoder_out, encoder_out_lens
 
     def encode_chunk(
@@ -340,32 +281,8 @@ class Paraformer(FunASRModel):
             if self.normalize is not None:
                 feats, feats_lengths = self.normalize(feats, feats_lengths)
 
-        # Pre-encoder, e.g. used for raw input data
-        if self.preencoder is not None:
-            feats, feats_lengths = self.preencoder(feats, feats_lengths)
-
         # 4. Forward encoder
-        # feats: (Batch, Length, Dim)
-        # -> encoder_out: (Batch, Length2, Dim2)
-        if self.encoder.interctc_use_conditioning:
-            encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(
-                feats, feats_lengths, cache=cache["encoder"], ctc=self.ctc
-            )
-        else:
-            encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(feats, feats_lengths, cache=cache["encoder"])
-        intermediate_outs = None
-        if isinstance(encoder_out, tuple):
-            intermediate_outs = encoder_out[1]
-            encoder_out = encoder_out[0]
-
-        # Post-encoder, e.g. NLU
-        if self.postencoder is not None:
-            encoder_out, encoder_out_lens = self.postencoder(
-                encoder_out, encoder_out_lens
-            )
-
-        if intermediate_outs is not None:
-            return (encoder_out, intermediate_outs), encoder_out_lens
+        encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(feats, feats_lengths, cache=cache["encoder"])
 
         return encoder_out, torch.tensor([encoder_out.size(1)])