diff --git a/egs/librispeech/ASR/tiny_transducer_ctc/export.py b/egs/librispeech/ASR/tiny_transducer_ctc/export.py
index 4117f7244..334dd011e 100755
--- a/egs/librispeech/ASR/tiny_transducer_ctc/export.py
+++ b/egs/librispeech/ASR/tiny_transducer_ctc/export.py
@@ -76,17 +76,17 @@ import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
+from train import add_model_arguments, get_params, get_transducer_model
+
 from icefall.checkpoint import (
     average_checkpoints,
     average_checkpoints_with_averaged_model,
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.lexicon import UniqLexicon
-from icefall.utils import str2bool
-from train import add_model_arguments, get_params, get_transducer_model
+from icefall.utils import num_tokens, str2bool
 
 
 def get_parser():
@@ -143,13 +143,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lang-dir",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500",
-        help="""The lang dir
-        It contains language related input files such as
-        "lexicon.txt"
-        """,
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt",
     )
 
     parser.add_argument(
@@ -189,17 +186,9 @@ def main():
 
     logging.info(f"device: {device}")
 
-    if "lang_bpe" in str(params.lang_dir):
-        sp = spm.SentencePieceProcessor()
-        sp.load(params.lang_dir + "/bpe.model")
-        # <blk> is defined in local/train_bpe_model.py
-        params.blank_id = sp.piece_to_id("<blk>")
-        params.vocab_size = sp.get_piece_size()
-    else:
-        assert "lang_phone" in str(params.lang_dir)
-        phone_lexicon = UniqLexicon(params.lang_dir)
-        params.blank_id = 0
-        params.vocab_size = max(phone_lexicon.tokens) + 1
+    token_table = k2.SymbolTable.from_file(params.tokens)
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 
diff --git a/egs/librispeech/ASR/zipformer_ctc/export.py b/egs/librispeech/ASR/zipformer_ctc/export.py
index 0ff50f128..4c46aea2c 100755
--- a/egs/librispeech/ASR/zipformer_ctc/export.py
+++ b/egs/librispeech/ASR/zipformer_ctc/export.py
@@ -23,6 +23,7 @@ import argparse
 import logging
 from pathlib import Path
 
+import k2
 import torch
 from scaling_converter import convert_scaled_to_non_scaled
 from train import add_model_arguments, get_ctc_model, get_params
@@ -33,8 +34,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.lexicon import Lexicon
-from icefall.utils import str2bool
+from icefall.utils import num_tokens, str2bool
 
 
 def get_parser():
@@ -90,11 +90,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lang-dir",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500",
-        help="""It contains language related input files such as "lexicon.txt"
-        """,
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt",
     )
 
     parser.add_argument(
@@ -113,17 +112,15 @@ def get_parser():
 def main():
     args = get_parser().parse_args()
     args.exp_dir = Path(args.exp_dir)
-    args.lang_dir = Path(args.lang_dir)
 
     params = get_params()
     params.update(vars(args))
 
-    logging.info(params)
+    token_table = k2.SymbolTable.from_file(params.tokens)
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
-    lexicon = Lexicon(params.lang_dir)
-    max_token_id = max(lexicon.tokens)
-    num_classes = max_token_id + 1  # +1 for the blank
-    params.vocab_size = num_classes
+    logging.info(params)
 
     device = torch.device("cpu")
     if torch.cuda.is_available():