fix exporting tal_csasr recipe

egs/tal_csasr/ASR/pruned_transducer_stateless5/export.py

@@ -23,7 +23,7 @@
 Usage:
 ./pruned_transducer_stateless5/export.py \
   --exp-dir ./pruned_transducer_stateless5/exp \
-  --lang-dir ./data/lang_char \
+  --tokens ./data/lang_char/tokens.txt \
   --epoch 30 \
   --avg 24 \
   --use-averaged-model True
@@ -50,8 +50,9 @@ import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
+from scaling_converter import convert_scaled_to_non_scaled
 from train import add_model_arguments, get_params, get_transducer_model
 
 from icefall.checkpoint import (
@@ -60,8 +61,7 @@ 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():
@@ -118,13 +118,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lang-dir",
+        "--tokens",
         type=str,
-        default="data/lang_char",
-        help="""The lang dir
-        It contains language related input files such as
-        "lexicon.txt"
-        """,
+        default="data/lang_char/tokens.txt",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -160,13 +157,14 @@ def main():
 
     logging.info(f"device: {device}")
 
-    bpe_model = params.lang_dir + "/bpe.model"
-    sp = spm.SentencePieceProcessor()
-    sp.load(bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
-    lexicon = Lexicon(params.lang_dir)
-    params.blank_id = lexicon.token_table["<blk>"]
-    params.vocab_size = max(lexicon.tokens) + 1
+    # Load id of the <blk> token and the vocab size
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = token_table["<blk>"]
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 
@@ -256,6 +254,7 @@ def main():
     model.eval()
 
     if params.jit:
+        convert_scaled_to_non_scaled(model, inplace=True)
         # We won't use the forward() method of the model in C++, so just ignore
         # it here.
         # Otherwise, one of its arguments is a ragged tensor and is not

egs/tal_csasr/ASR/pruned_transducer_stateless5/lstmp.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/lstm_transducer_stateless2/lstmp.py

egs/tal_csasr/ASR/pruned_transducer_stateless5/scaling_converter.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless3/scaling_converter.py