diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp
index fdca8c5ac..9d4f01465 100644
Binary files a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp and b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp differ
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py
index 8cae787ab..20adb7724 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py
@@ -465,13 +465,6 @@ class Data2VecAudioModel(BaseFairseqModel):
 
         features = features.transpose(1, 2)
 
-        ## for prompt tuning
-        if prompt is not None:
-            #features = torch.cat([features, prompt])
-            features = torch.cat([prompt, features])
-
-        features = self.layer_norm(features)
-
         orig_padding_mask = padding_mask
 
         if padding_mask is not None and padding_mask.any():
@@ -495,6 +488,13 @@ class Data2VecAudioModel(BaseFairseqModel):
         else:
             padding_mask = None
         
+        ## for prompt tuning
+        if prompt is not None:
+            #features = torch.cat([features, prompt])
+            features = torch.cat([prompt, features])
+
+        features = self.layer_norm(features)
+
         #print(padding_mask.size())
         #print((padding_mask[0] == True).nonzero(as_tuple=True)[0])
         #print((padding_mask[1] == True).nonzero(as_tuple=True)[0][1])