diff --git a/egs/librispeech/ASR/incremental_transf/.identity_train.py.swp b/egs/librispeech/ASR/incremental_transf/.identity_train.py.swp
index fd33ee622..65b825032 100644
Binary files a/egs/librispeech/ASR/incremental_transf/.identity_train.py.swp and b/egs/librispeech/ASR/incremental_transf/.identity_train.py.swp differ
diff --git a/egs/librispeech/ASR/incremental_transf/identity_train.py b/egs/librispeech/ASR/incremental_transf/identity_train.py
index 66e59465c..45a5d8e08 100755
--- a/egs/librispeech/ASR/incremental_transf/identity_train.py
+++ b/egs/librispeech/ASR/incremental_transf/identity_train.py
@@ -665,6 +665,7 @@ def compute_loss(
     y = k2.RaggedTensor(y).to(device)
 
     with torch.set_grad_enabled(is_training):
+        '''
         simple_loss, pruned_loss = model(
             x=feature,
             x_lens=feature_lens,
@@ -678,36 +679,18 @@ def compute_loss(
         )
         simple_loss_is_finite = torch.isfinite(simple_loss)
         pruned_loss_is_finite = torch.isfinite(pruned_loss)
-        is_finite = simple_loss_is_finite & pruned_loss_is_finite
-        if not torch.all(is_finite):
-            logging.info(
-                "Not all losses are finite!\n"
-                f"simple_loss: {simple_loss}\n"
-                f"pruned_loss: {pruned_loss}"
-            )
-            display_and_save_batch(batch, params=params, sp=sp)
-            simple_loss = simple_loss[simple_loss_is_finite]
-            pruned_loss = pruned_loss[pruned_loss_is_finite]
+        '''
+        mse_loss = model(
+                x=feature,
+                x_lens=feautre_lens,
+        )
 
-            # If the batch contains more than 10 utterances AND
-            # if either all simple_loss or pruned_loss is inf or nan,
-            # we stop the training process by raising an exception
-            if torch.all(~simple_loss_is_finite) or torch.all(~pruned_loss_is_finite):
-                raise ValueError(
-                    "There are too many utterances in this batch "
-                    "leading to inf or nan losses."
-                )
-
-        simple_loss = simple_loss.sum()
-        pruned_loss = pruned_loss.sum()
         # after the main warmup step, we keep pruned_loss_scale small
         # for the same amount of time (model_warm_step), to avoid
         # overwhelming the simple_loss and causing it to diverge,
         # in case it had not fully learned the alignment yet.
-        pruned_loss_scale = (
-            0.0 if warmup < 1.0 else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
-        )
-        loss = params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
+        #loss = params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
+        loss = mse_loss
 
     assert loss.requires_grad == is_training