diff --git a/egs/aishell/ASR/transducer_stateless/train.py b/egs/aishell/ASR/transducer_stateless/train.py
index 21128318b..d54157709 100755
--- a/egs/aishell/ASR/transducer_stateless/train.py
+++ b/egs/aishell/ASR/transducer_stateless/train.py
@@ -604,21 +604,18 @@ def run(rank, world_size, args):
     train_cuts = aishell.train_cuts()
 
     def remove_short_and_long_utt(c: Cut):
-        # Keep only utterances with duration between 1 second and 20 seconds
-        return 1.0 <= c.duration <= 20.0
-
-    num_in_total = len(train_cuts)
+        # Keep only utterances with duration between 1 second and 12 seconds
+        #
+        # Caution: There is a reason to select 12.0 here. Please see
+        # ../local/display_manifest_statistics.py
+        #
+        # You should use ../local/display_manifest_statistics.py to get
+        # an utterance duration distribution for your dataset to select
+        # the threshold
+        return 1.0 <= c.duration <= 12.0
 
     train_cuts = train_cuts.filter(remove_short_and_long_utt)
 
-    num_left = len(train_cuts)
-    num_removed = num_in_total - num_left
-    removed_percent = num_removed / num_in_total * 100
-
-    logging.info(f"Before removing short and long utterances: {num_in_total}")
-    logging.info(f"After removing short and long utterances: {num_left}")
-    logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)")
-
     train_dl = aishell.train_dataloaders(train_cuts)
     valid_dl = aishell.valid_dataloaders(aishell.valid_cuts())
 
diff --git a/egs/aishell/ASR/transducer_stateless_modified-2/train.py b/egs/aishell/ASR/transducer_stateless_modified-2/train.py
index 0975f309a..962fffdf5 100755
--- a/egs/aishell/ASR/transducer_stateless_modified-2/train.py
+++ b/egs/aishell/ASR/transducer_stateless_modified-2/train.py
@@ -640,7 +640,7 @@ def train_one_epoch(
 
 def filter_short_and_long_utterances(cuts: CutSet) -> CutSet:
     def remove_short_and_long_utt(c: Cut):
-        # Keep only utterances with duration between 1 second and 20 seconds
+        # Keep only utterances with duration between 1 second and 12 seconds
         #
         # Caution: There is a reason to select 12.0 here. Please see
         # ../local/display_manifest_statistics.py
diff --git a/egs/aishell/ASR/transducer_stateless_modified/train.py b/egs/aishell/ASR/transducer_stateless_modified/train.py
index dcbc874a0..d3ffccafa 100755
--- a/egs/aishell/ASR/transducer_stateless_modified/train.py
+++ b/egs/aishell/ASR/transducer_stateless_modified/train.py
@@ -630,20 +630,17 @@ def run(rank, world_size, args):
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 12 seconds
+        #
+        # Caution: There is a reason to select 12.0 here. Please see
+        # ../local/display_manifest_statistics.py
+        #
+        # You should use ../local/display_manifest_statistics.py to get
+        # an utterance duration distribution for your dataset to select
+        # the threshold
         return 1.0 <= c.duration <= 12.0
 
-    num_in_total = len(train_cuts)
-
     train_cuts = train_cuts.filter(remove_short_and_long_utt)
 
-    num_left = len(train_cuts)
-    num_removed = num_in_total - num_left
-    removed_percent = num_removed / num_in_total * 100
-
-    logging.info(f"Before removing short and long utterances: {num_in_total}")
-    logging.info(f"After removing short and long utterances: {num_left}")
-    logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)")
-
     train_dl = aishell.train_dataloaders(train_cuts)
     valid_dl = aishell.valid_dataloaders(aishell.valid_cuts())