diff --git a/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py b/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py
index 17df09cc8..a828bead9 100755
--- a/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py
+++ b/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py
@@ -91,7 +91,7 @@ def get_parser():
 
 def compute_fbank_wenetspeech_splits(args):
     subset = args.training_subset
-    subset = str(subset) 
+    subset = str(subset)
     num_splits = args.num_splits
     output_dir = f"data/fbank/{subset}_split_{num_splits}"
     output_dir = Path(output_dir)
diff --git a/egs/wenetspeech/ASR/local/display_manifest_statistics.py b/egs/wenetspeech/ASR/local/display_manifest_statistics.py
index a94c2d9ab..30dc5a5ec 100644
--- a/egs/wenetspeech/ASR/local/display_manifest_statistics.py
+++ b/egs/wenetspeech/ASR/local/display_manifest_statistics.py
@@ -31,12 +31,11 @@ from lhotse import load_manifest
 
 def main():
     paths = [
-        #"./data/fbank/cuts_S.jsonl.gz",
-        #"./data/fbank/cuts_M.jsonl.gz",
-        "./data/fbank/cuts_L.jsonl.gz",
-        #"./data/fbank/cuts_DEV.jsonl.gz",
-        #"./data/fbank/cuts_TEST_NET.jsonl.gz",
-        #"./data/fbank/cuts_TEST_MEETING.jsonl.gz"
+        "./data/fbank/cuts_S.jsonl.gz",
+        "./data/fbank/cuts_M.jsonl.gz",
+        "./data/fbank/cuts_DEV.jsonl.gz",
+        "./data/fbank/cuts_TEST_NET.jsonl.gz",
+        "./data/fbank/cuts_TEST_MEETING.jsonl.gz",
     ]
 
     for path in paths:
diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py
index 123f562e3..d2f8d85ce 100644
--- a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py
+++ b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py
@@ -338,7 +338,7 @@ class WenetSpeechAsrDataModule:
         if sampler_state_dict is not None:
             logging.info("Loading sampler state dict")
             train_dl.sampler.load_state_dict(sampler_state_dict)
-        
+
         return train_dl
 
     def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless2/beam_search.py b/egs/wenetspeech/ASR/pruned_transducer_stateless2/beam_search.py
index d3ac460c7..2e9bf3e0b 100644
--- a/egs/wenetspeech/ASR/pruned_transducer_stateless2/beam_search.py
+++ b/egs/wenetspeech/ASR/pruned_transducer_stateless2/beam_search.py
@@ -503,8 +503,7 @@ def modified_beam_search(
         for i in range(batch_size):
             topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)
 
-            #topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
-            topk_hyp_indexes = torch.div(topk_indexes, vocab_size, rounding_mode="trunc")
+            topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
             topk_token_indexes = (topk_indexes % vocab_size).tolist()
 
             for k in range(len(topk_hyp_indexes)):
diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless2/train.py b/egs/wenetspeech/ASR/pruned_transducer_stateless2/train.py
index e6d170a45..9980559bf 100644
--- a/egs/wenetspeech/ASR/pruned_transducer_stateless2/train.py
+++ b/egs/wenetspeech/ASR/pruned_transducer_stateless2/train.py
@@ -900,7 +900,7 @@ def run(rank, world_size, args):
     train_dl = wenetspeech.train_dataloaders(
         train_cuts, sampler_state_dict=sampler_state_dict
     )
-    
+
     if not params.print_diagnostics and params.start_batch == 0:
         scan_pessimistic_batches_for_oom(
             model=model,
@@ -909,7 +909,7 @@ def run(rank, world_size, args):
             graph_compiler=graph_compiler,
             params=params,
         )
-    
+
     scaler = GradScaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")