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 b1d6479e3..43f705f85 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/.train_lora.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_lora.py.swp
index 7b9e471f6..3bc9274df 100644
Binary files a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_lora.py.swp and b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_lora.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 021502339..d57f74937 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
@@ -138,7 +138,6 @@ class LoRAModule(nn.Module):
     def __init__(
         self,
         embedding_dim: float = 768,
-        layer_num: int = 12,
         rank: int = 16,
         lora_alpha: int = 1,
         lora_dropout: float = 0.1,
@@ -153,10 +152,12 @@ class LoRAModule(nn.Module):
         else:
             self.lora_dropout = lambda x: x
 
-        self.lora_A = nn.ModuleList(
-                            [nn.Linear(embedding_dim, self.r) for _ in range(layer_num)])
-        self.lora_B = nn.ModuleList(
-                            [nn.Linear(self.r, embedding_dim) for _ in range(layer_num)])
+        #self.lora_A = nn.ModuleList(
+        #                    [nn.Linear(embedding_dim, self.r) for _ in range(layer_num)])
+        #self.lora_B = nn.ModuleList(
+        #                    [nn.Linear(self.r, embedding_dim) for _ in range(layer_num)])
+        self.lora_A = nn.Linear(embedding_dim, self.r)
+        self.lora_B = nn.Linear(self.r, embedding_dim)
         self.scaling = self.lora_alpha / self.r
         self.reset_parameters()