diff --git a/funasr/models/monotonic_aligner/model.py b/funasr/models/monotonic_aligner/model.py
index ece319dea..cc03ee46a 100644
--- a/funasr/models/monotonic_aligner/model.py
+++ b/funasr/models/monotonic_aligner/model.py
@@ -4,11 +4,13 @@ import torch
 from torch.cuda.amp import autocast
 from typing import Union, Dict, List, Tuple, Optional
 
+from funasr.register import tables
+from funasr.utils import postprocess_utils
+from funasr.utils.datadir_writer import DatadirWriter
 from funasr.models.paraformer.cif_predictor import mae_loss
-from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
-from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
-from funasr.metrics.compute_acc import th_accuracy
 from funasr.train_utils.device_funcs import force_gatherable
+from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
+from funasr.models.transformer.utils.nets_utils import make_pad_mask
 from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
 from funasr.utils import postprocess_utils
 from funasr.utils.datadir_writer import DatadirWriter
@@ -25,7 +27,6 @@ class MonotonicAligner(torch.nn.Module):
     Achieving timestamp prediction while recognizing with non-autoregressive end-to-end ASR model
     https://arxiv.org/abs/2301.12343
     """
-
     def __init__(
         self,
         input_size: int = 80,
@@ -41,7 +42,6 @@ class MonotonicAligner(torch.nn.Module):
         length_normalized_loss: bool = False,
         **kwargs,
     ):
-
         super().__init__()
 
         if specaug is not None:
@@ -155,7 +155,6 @@ class MonotonicAligner(torch.nn.Module):
              frontend=None,
              **kwargs,
              ):
-        
         meta_data = {}
         # extract fbank feats
         time1 = time.perf_counter()
@@ -190,8 +189,7 @@ class MonotonicAligner(torch.nn.Module):
             timestamp_str, timestamp = ts_prediction_lfr6_standard(us_alpha[:encoder_out_lens[i] * 3],
                                                                    us_peak[:encoder_out_lens[i] * 3],
                                                                    copy.copy(token))
-            text_postprocessed, time_stamp_postprocessed, word_lists = postprocess_utils.sentence_postprocess(
-                token, timestamp)
+            text_postprocessed, time_stamp_postprocessed, _ = postprocess_utils.sentence_postprocess(token, timestamp)
             result_i = {"key": key[i], "text": text_postprocessed,
                                 "timestamp": time_stamp_postprocessed,
                                 }