diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_adapter.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_adapter.py.swp
index 15e53574e..1e53d130d 100644
Binary files a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_adapter.py.swp and b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_adapter.py.swp differ
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py
index 9c5bd69a6..c51e753c7 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py
@@ -835,7 +835,10 @@ def compute_loss(
 
     #texts = batch["supervisions"]["text"]
     texts = []
-    for utt_id in 
+    for utt_id in batch["id"]:
+        print(utt_id)
+        print(pl_texts[utt_id])
+        texts.append(pl_texts[utt_id])
     
     token_ids = sp.encode(texts, out_type=int)
     y = k2.RaggedTensor(token_ids).to(device)