Update export-onnx.py

updated `export-oonx.py` to accept `tokens.txt` for blank_id and vocab_size

egs/librispeech/ASR/pruned_transducer_stateless7/export-onnx.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 #
-# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
+# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang
+#                                            Zengrui Jin)
 
 """
 This script exports a transducer model from PyTorch to ONNX.
@@ -28,7 +29,7 @@ popd
 2. Export the model to ONNX
 
 ./pruned_transducer_stateless7/export-onnx.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --use-averaged-model 0 \
   --epoch 99 \
   --avg 1 \
@@ -49,9 +50,10 @@ import argparse
 import logging
 from pathlib import Path
 from typing import Dict, Tuple
+import re
 
+import k2
 import onnx
-import sentencepiece as spm
 import torch
 import torch.nn as nn
 from decoder import Decoder
@@ -123,10 +125,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
+        help="Path to the tokens.txt.",
-        help="Path to the BPE model",
     )
 
     parser.add_argument(
@@ -159,6 +160,26 @@ def add_meta_data(filename: str, meta_data: Dict[str, str]):
     onnx.save(model, filename)
 
 
+def num_tokens(
+    token_table: k2.SymbolTable, disambig_pattern: str = re.compile(r"^#\d+$")
+) -> int:
+    """Return the number of tokens excluding those from
+    disambiguation symbols.
+
+    Caution:
+      0 is not a token ID so it is excluded from the return value.
+    """
+    symbols = token_table.symbols
+    ans = []
+    for s in symbols:
+        if not disambig_pattern.match(s):
+            ans.append(token_table[s])
+    num_tokens = len(ans)
+    if 0 in ans:
+        num_tokens -= 1
+    return num_tokens
+
+
 class OnnxEncoder(nn.Module):
     """A wrapper for Zipformer and the encoder_proj from the joiner"""
 
@@ -411,12 +432,12 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
-    # <blk> is defined in local/train_bpe_model.py
+    # Load id of the <blk> token and the vocab size
-    params.blank_id = sp.piece_to_id("<blk>")
+    params.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)