diff --git a/egs/librispeech/ASR/.run_v2.sh.swp b/egs/librispeech/ASR/.run_v2.sh.swp
index c1ec03e7c..17cc27322 100644
Binary files a/egs/librispeech/ASR/.run_v2.sh.swp and b/egs/librispeech/ASR/.run_v2.sh.swp differ
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train.py.swp
index 468ac8578..b5713ff2b 100644
Binary files a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train.py.swp and b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train.py.swp differ
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
index ffc257b56..7d62876a8 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
@@ -719,41 +719,44 @@ def compute_loss(
 
         loss = simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
     
-    '''
-    # Compute ctc loss
+    info = MetricsTracker()
+    
+    if params.ctc_loss_scale > 0:
+        # Compute ctc loss
 
-    # NOTE: We need `encode_supervisions` to sort sequences with
-    # different duration in decreasing order, required by
-    # `k2.intersect_dense` called in `k2.ctc_loss`
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        supervision_segments, token_ids = encode_supervisions(
-            supervisions,
-            subsampling_factor=params.subsampling_factor,
-            token_ids=token_ids,
+        # NOTE: We need `encode_supervisions` to sort sequences with
+        # different duration in decreasing order, required by
+        # `k2.intersect_dense` called in `k2.ctc_loss`
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            supervision_segments, token_ids = encode_supervisions(
+                supervisions,
+                subsampling_factor=params.subsampling_factor,
+                token_ids=token_ids,
+            )
+
+        # Works with a BPE model
+        decoding_graph = k2.ctc_graph(token_ids, modified=False, device=device)
+        dense_fsa_vec = k2.DenseFsaVec(
+            ctc_output,
+            supervision_segments,
+            allow_truncate=params.subsampling_factor - 1,
         )
 
-    # Works with a BPE model
-    decoding_graph = k2.ctc_graph(token_ids, modified=False, device=device)
-    dense_fsa_vec = k2.DenseFsaVec(
-        ctc_output,
-        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="sum",
-        use_double_scores=params.use_double_scores,
-    )
-    assert ctc_loss.requires_grad == is_training
-    loss += params.ctc_loss_scale * ctc_loss
-    '''
+        ctc_loss = k2.ctc_loss(
+            decoding_graph=decoding_graph,
+            dense_fsa_vec=dense_fsa_vec,
+            output_beam=params.beam_size,
+            reduction="sum",
+            use_double_scores=params.use_double_scores,
+        )
+        assert ctc_loss.requires_grad == is_training
+        loss += params.ctc_loss_scale * ctc_loss
+    
+        info["ctc_loss"] = ctc_loss.detach().cpu().item()
+    
     assert loss.requires_grad == is_training
 
-    info = MetricsTracker()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
         info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
@@ -762,7 +765,6 @@ def compute_loss(
     info["loss"] = loss.detach().cpu().item()
     info["simple_loss"] = simple_loss.detach().cpu().item()
     info["pruned_loss"] = pruned_loss.detach().cpu().item()
-    #info["ctc_loss"] = ctc_loss.detach().cpu().item()
 
     return loss, info