minor fixes

egs/librispeech/ASR/lstm_transducer_stateless2/export-onnx-zh.py

@@ -28,7 +28,7 @@ popd
 2. Export the model to ONNX
 
 ./lstm_transducer_stateless2/export-onnx-zh.py \
-  --lang-dir ./icefall-asr-wenetspeech-lstm-transducer-stateless-2022-10-14/data/lang_char \
+  --tokens ./icefall-asr-wenetspeech-lstm-transducer-stateless-2022-10-14/data/lang_char/tokens.txt \
   --use-averaged-model 1 \
   --epoch 11 \
   --avg 1 \
@@ -55,6 +55,7 @@ import logging
 from pathlib import Path
 from typing import Dict, Optional, Tuple
 
+import k2
 import onnx
 import torch
 import torch.nn as nn
@@ -70,8 +71,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.lexicon import Lexicon
-from icefall.utils import setup_logger, str2bool
+from icefall.utils import num_tokens, setup_logger, str2bool
 
 
 def get_parser():
@@ -128,10 +128,10 @@ 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(
@@ -441,9 +441,9 @@ 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)
 

egs/swbd/ASR/conformer_ctc/export.py

@@ -118,7 +118,7 @@ def main():
         num_features=params.feature_dim,
         nhead=params.nhead,
         d_model=params.attention_dim,
-        num_classes=num_classes,
+        num_classes=params.vocab_size,
         subsampling_factor=params.subsampling_factor,
         num_decoder_layers=params.num_decoder_layers,
         vgg_frontend=False,

egs/tedlium3/ASR/conformer_ctc2/export.py

@@ -182,7 +182,7 @@ def main():
 
     model = Conformer(
         num_features=params.feature_dim,
-        num_classes=num_classes,
+        num_classes=params.vocab_size,
         subsampling_factor=params.subsampling_factor,
         d_model=params.dim_model,
         nhead=params.nhead,