diff --git a/egs/librispeech/ASR/conformer_ctc/decode.py b/egs/librispeech/ASR/conformer_ctc/decode.py
index 6b0ae0931..4f22a6f6c 100755
--- a/egs/librispeech/ASR/conformer_ctc/decode.py
+++ b/egs/librispeech/ASR/conformer_ctc/decode.py
@@ -346,8 +346,7 @@ def decode_dataset(
 
         if batch_idx % 100 == 0:
             logging.info(
-                f"batch {batch_idx}, cuts processed until now is "
-                f"{num_cuts} "
+                f"batch {batch_idx}, cuts processed until now is {num_cuts}"
             )
     return results
 
@@ -430,7 +429,9 @@ def main():
     sos_id = graph_compiler.sos_id
     eos_id = graph_compiler.eos_id
 
-    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt"))
+    HLG = k2.Fsa.from_dict(
+        torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu")
+    )
     HLG = HLG.to(device)
     assert HLG.requires_grad is False
 
@@ -461,7 +462,7 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt")
+            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location="cpu")
             G = k2.Fsa.from_dict(d).to(device)
 
         if params.method in ["whole-lattice-rescoring", "attention-decoder"]:
diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
index 137fa795c..9a1aad579 100755
--- a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
+++ b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
@@ -236,7 +236,6 @@ def decode_dataset(
     results = []
 
     num_cuts = 0
-    tot_num_cuts = len(dl.dataset.cuts)
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -264,9 +263,7 @@ def decode_dataset(
 
         if batch_idx % 100 == 0:
             logging.info(
-                f"batch {batch_idx}, cuts processed until now is "
-                f"{num_cuts}/{tot_num_cuts} "
-                f"({float(num_cuts)/tot_num_cuts*100:.6f}%)"
+                f"batch {batch_idx}, cuts processed until now is {num_cuts}"
             )
     return results
 
@@ -328,7 +325,9 @@ def main():
 
     logging.info(f"device: {device}")
 
-    HLG = k2.Fsa.from_dict(torch.load("data/lang_phone/HLG.pt"))
+    HLG = k2.Fsa.from_dict(
+        torch.load("data/lang_phone/HLG.pt", map_location="cpu")
+    )
     HLG = HLG.to(device)
     assert HLG.requires_grad is False
 
@@ -355,7 +354,7 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt")
+            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location="cpu")
             G = k2.Fsa.from_dict(d).to(device)
 
         if params.method == "whole-lattice-rescoring":