diff --git a/egs/aishell/ASR/transducer_stateless/export.py b/egs/aishell/ASR/transducer_stateless/export.py
index 5687260dfb..591b333e04 100755
--- a/egs/aishell/ASR/transducer_stateless/export.py
+++ b/egs/aishell/ASR/transducer_stateless/export.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 #
 # Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
+#           2022 Xiaomi Corporation (Author: Mingshuang Luo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -22,7 +23,7 @@
 Usage:
 ./transducer_stateless/export.py \
   --exp-dir ./transducer_stateless/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --lang-dir data/lang_char \
   --epoch 20 \
   --avg 10
 
@@ -33,20 +34,19 @@
     cd /path/to/exp_dir
     ln -s pretrained.pt epoch-9999.pt
 
-    cd /path/to/egs/librispeech/ASR
+    cd /path/to/egs/aishell/ASR
     ./transducer_stateless/decode.py \
         --exp-dir ./transducer_stateless/exp \
         --epoch 9999 \
         --avg 1 \
         --max-duration 1 \
-        --bpe-model data/lang_bpe_500/bpe.model
+        --lang-dir data/lang_char
 """
 
 import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
 import torch
 import torch.nn as nn
 from conformer import Conformer
@@ -56,6 +56,7 @@
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
 from icefall.env import get_env_info
+from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, str2bool
 
 
@@ -91,10 +92,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--lang-dir",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_char",
+        help="The lang dir",
     )
 
     parser.add_argument(
@@ -194,12 +195,10 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    lexicon = Lexicon(params.lang_dir)
 
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
+    params.blank_id = 0
+    params.vocab_size = max(lexicon.tokens) + 1
 
     logging.info(params)