diff --git a/egs/tedlium2/ASR/conformer_ctc3/.decode.py.swp b/egs/tedlium2/ASR/conformer_ctc3/.decode.py.swp
index 03ea403b1..2f9ba22fe 100644
Binary files a/egs/tedlium2/ASR/conformer_ctc3/.decode.py.swp and b/egs/tedlium2/ASR/conformer_ctc3/.decode.py.swp differ
diff --git a/egs/tedlium2/ASR/conformer_ctc3/.train.py.swp b/egs/tedlium2/ASR/conformer_ctc3/.train.py.swp
index ea41fd560..547e2af73 100644
Binary files a/egs/tedlium2/ASR/conformer_ctc3/.train.py.swp and b/egs/tedlium2/ASR/conformer_ctc3/.train.py.swp differ
diff --git a/egs/tedlium2/ASR/conformer_ctc3/train.py b/egs/tedlium2/ASR/conformer_ctc3/train.py
index 991b5c0e7..e41ffeebd 100755
--- a/egs/tedlium2/ASR/conformer_ctc3/train.py
+++ b/egs/tedlium2/ASR/conformer_ctc3/train.py
@@ -688,62 +688,6 @@ def compute_loss(
                 use_double_scores=params.use_double_scores,
             )
 
-            '''
-            dense_fsa_vec = k2.DenseFsaVec(
-                nnet_output[0],
-                supervision_segments,
-                allow_truncate=params.subsampling_factor - 1,
-            )
-            
-            dense_fsa_vec_inter = [
-                #k2.DenseFsaVec(
-                #    nnet_output[1][2],
-                #    supervision_segments,
-                #    allow_truncate=params.subsampling_factor - 1,
-                #),
-                #k2.DenseFsaVec(
-                #    nnet_output[1][5],
-                #    supervision_segments,
-                #    allow_truncate=params.subsampling_factor - 1,
-                #),
-                k2.DenseFsaVec(
-                    nnet_output[1][8],
-                    supervision_segments,
-                    allow_truncate=params.subsampling_factor - 1,
-                ),
-                #k2.DenseFsaVec(
-                #    nnet_output[1][11],
-                #    supervision_segments,
-                #    allow_truncate=params.subsampling_factor - 1,
-                #),
-                #k2.DenseFsaVec(
-                #    nnet_output[1][14],
-                #    supervision_segments,
-                #    allow_truncate=params.subsampling_factor - 1,
-                #)
-            ]
-
-            ctc_loss = k2.ctc_loss(
-                decoding_graph=decoding_graph,
-                dense_fsa_vec=dense_fsa_vec,
-                output_beam=params.beam_size,
-                reduction=params.reduction,
-                use_double_scores=params.use_double_scores,
-            ) 
-            
-            inter_ctc_loss = 0
-            for fsa in dense_fsa_vec_inter:
-                inter_ctc_loss += k2.ctc_loss(
-                    decoding_graph=decoding_graph,
-                    dense_fsa_vec=fsa,
-                    output_beam=params.beam_size,
-                    reduction=params.reduction,
-                    use_double_scores=params.use_double_scores,
-                ) 
-            
-            ctc_loss = (1-params.interctc_weight) * ctc_loss + params.interctc_weight * inter_ctc_loss
-            '''
-
         if params.att_rate > 0.0:
             with torch.set_grad_enabled(is_training):
                 mmodel = model.module if hasattr(model, "module") else model