diff --git a/egs/audioset/AT/zipformer/export.py b/egs/audioset/AT/zipformer/export.py
index 83034df95..1c0e702d6 100755
--- a/egs/audioset/AT/zipformer/export.py
+++ b/egs/audioset/AT/zipformer/export.py
@@ -82,9 +82,8 @@ Check ./pretrained.py for its usage.
 import argparse
 import logging
 from pathlib import Path
-from typing import List, Tuple
+from typing import Tuple
 
-import k2
 import torch
 from scaling_converter import convert_scaled_to_non_scaled
 from torch import Tensor, nn
@@ -96,7 +95,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import make_pad_mask, num_tokens, str2bool
+from icefall.utils import make_pad_mask, str2bool
 
 
 def get_parser():
@@ -302,7 +301,6 @@ def main():
         # torch scriptabe.
         model.__class__.forward = torch.jit.ignore(model.__class__.forward)
 
-        
         model.encoder = EncoderModel(model.encoder, model.encoder_embed)
         filename = "jit_script.pt"
 
diff --git a/egs/audioset/AT/zipformer/pretrained.py b/egs/audioset/AT/zipformer/pretrained.py
index e3961736c..a162a8bb6 100755
--- a/egs/audioset/AT/zipformer/pretrained.py
+++ b/egs/audioset/AT/zipformer/pretrained.py
@@ -48,16 +48,12 @@ import logging
 import math
 from typing import List
 
-import k2
 import kaldifeat
 import torch
 import torchaudio
-from export import num_tokens
 from torch.nn.utils.rnn import pad_sequence
 from train import add_model_arguments, get_model, get_params
 
-from icefall.utils import make_pad_mask
-
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -189,11 +185,12 @@ def main():
     encoder_out, encoder_out_lens = model.forward_encoder(features, feature_lengths)
     logits = model.forward_audio_tagging(encoder_out, encoder_out_lens)
 
-    results = []
     for i, logit in enumerate(logits):
         topk_prob, topk_index = logit.sigmoid().topk(5)
         topk_labels = [label_dict[index.item()] for index in topk_index]
-        print(f"Top 5 predicted labels of the {i} th audio are {topk_labels} with probability of {topk_prob.tolist()}")
+        print(
+            f"Top 5 predicted labels of the {i} th audio are {topk_labels} with probability of {topk_prob.tolist()}"
+        )
 
     logging.info("Decoding Done")