diff --git a/icefall/decode.py b/icefall/decode.py
index d3e420eec..49b7d4f3b 100644
--- a/icefall/decode.py
+++ b/icefall/decode.py
@@ -824,15 +824,41 @@ def rescore_with_attention_decoder(
       ngram_lm_scale_attention_scale and the value is the
       best decoding path for each utterance in the lattice.
     """
-    nbest = Nbest.from_lattice(
-        lattice=lattice,
-        num_paths=num_paths,
-        use_double_scores=use_double_scores,
-        nbest_scale=nbest_scale,
-    )
-    # nbest.fsa.scores are all 0s at this point
+    max_loop_count = 10
+    loop_count = 0
+    while loop_count <= max_loop_count:
+        try:
+            nbest = Nbest.from_lattice(
+                lattice=lattice,
+                num_paths=num_paths,
+                use_double_scores=use_double_scores,
+                nbest_scale=nbest_scale,
+            )
+            # nbest.fsa.scores are all 0s at this point
+            nbest = nbest.intersect(lattice)
+            break
+        except RuntimeError as e:
+            logging.info(f"Caught exception:\n{e}\n")
+            logging.info(
+                f"num_paths before decreasing: {num_paths}"
+            )
+            num_paths = int(num_paths / 2)
+            if loop_count >= max_loop_count or num_paths <= 0:
+                logging.info(
+                    "Return None as the resulting lattice is too large."
+                )
+                return None
+            logging.info(
+                "This OOM is not an error. You can ignore it. "
+                "If your model does not converge well, or --max-duration "
+                "is too large, or the input sound file is difficult to "
+                "decode, you will meet this exception."
+            )
+            logging.info(
+                f"num_paths after decreasing: {num_paths}"
+            )
+        loop_count += 1
 
-    nbest = nbest.intersect(lattice)
     # Now nbest.fsa has its scores set.
     # Also, nbest.fsa inherits the attributes from `lattice`.
     assert hasattr(nbest.fsa, "lm_scores")