Update export-onnx.py

egs/wenetspeech/ASR/pruned_transducer_stateless2/export-onnx.py

@@ -28,7 +28,7 @@ popd
 2. Export the model to ONNX
 
 ./pruned_transducer_stateless2/export-onnx.py \
-  --lang-dir $repo/data/lang_char \
+  --tokens $repo/data/lang_char/tokens.txt \
   --epoch 99 \
   --avg 1 \
   --exp-dir $repo/exp
@@ -48,6 +48,7 @@ import logging
 from pathlib import Path
 from typing import Dict, Tuple
 
+import k2
 import onnx
 import torch
 import torch.nn as nn
@@ -57,14 +58,8 @@ from onnxruntime.quantization import QuantType, quantize_dynamic
 from scaling_converter import convert_scaled_to_non_scaled
 from train import get_params, get_transducer_model
 
-from icefall.checkpoint import (
-    average_checkpoints,
-    average_checkpoints_with_averaged_model,
-    find_checkpoints,
-    load_checkpoint,
-)
-from icefall.lexicon import Lexicon
-from icefall.utils import setup_logger, str2bool
+from icefall.checkpoint import average_checkpoints, load_checkpoint
+from icefall.utils import num_tokens, setup_logger, str2bool
 
 
 def get_parser():
@@ -110,10 +105,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lang-dir",
+        "--tokens",
         type=str,
-        default="data/lang_char",
-        help="The lang dir",
+        default="data/lang_char/tokens.txt",
+        help="Path to the tokens.txt",
     )
 
     parser.add_argument(
@@ -397,9 +392,9 @@ def main():
 
     logging.info(f"device: {device}")
 
-    lexicon = Lexicon(params.lang_dir)
-    params.blank_id = 0
-    params.vocab_size = max(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
 
     logging.info(params)