diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/.train.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless5/.train.py.swp
index 7d2e96212..9607aa4bd 100644
Binary files a/egs/librispeech/ASR/pruned_transducer_stateless5/.train.py.swp and b/egs/librispeech/ASR/pruned_transducer_stateless5/.train.py.swp differ
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/train.py b/egs/librispeech/ASR/pruned_transducer_stateless5/train.py
index 847c80ab0..567ceafa7 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless5/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless5/train.py
@@ -1024,9 +1024,9 @@ def run(rank, world_size, args):
         # an utterance duration distribution for your dataset to select
         # the threshold
         if c.duration < 1.0 or c.duration > 20.0:
-            logging.warning(
-                f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
-            )
+            #logging.warning(
+            #    f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
+            #)
             return False
 
         # In pruned RNN-T, we require that T >= S
@@ -1067,7 +1067,8 @@ def run(rank, world_size, args):
     valid_cuts = librispeech.dev_clean_cuts()
     valid_cuts += librispeech.dev_other_cuts()
     valid_dl = librispeech.valid_dataloaders(valid_cuts)
-
+    
+    '''
     if params.start_batch <= 0 and not params.print_diagnostics:
         scan_pessimistic_batches_for_oom(
             model=model,
@@ -1077,6 +1078,7 @@ def run(rank, world_size, args):
             params=params,
             warmup=0.0 if params.start_epoch == 1 else 1.0,
         )
+    '''
 
     scaler = GradScaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints: