minor fixes

.github/scripts/run-librispeech-conformer-ctc3-2022-11-28.sh

@@ -40,7 +40,7 @@ for m in ctc-decoding 1best; do
     --model-filename $repo/exp/jit_trace.pt \
     --words-file $repo/data/lang_bpe_500/words.txt \
     --HLG $repo/data/lang_bpe_500/HLG.pt \
-    --tokens $repo/data/lang_bpe_500/tokens.txt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
     --G $repo/data/lm/G_4_gram.pt \
     --method $m \
     --sample-rate 16000 \

.github/scripts/test-ncnn-export.sh

@@ -60,7 +60,7 @@ log "Export via torch.jit.trace()"
   --epoch 99 \
   --avg 1 \
   --use-averaged-model 0 \
-  \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --num-encoder-layers 12 \
   --chunk-length 32 \
   --cnn-module-kernel 31 \

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export.py

@@ -139,8 +139,8 @@ import argparse
 import logging
 from pathlib import Path
 
+import k2
 import onnxruntime
-import sentencepiece as spm
 import torch
 import torch.nn as nn
 from onnx_model_wrapper import OnnxStreamingEncoder, TritonOnnxDecoder, TritonOnnxJoiner
@@ -154,7 +154,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import str2bool
+from icefall.utils import num_tokens, str2bool
 
 
 def get_parser():
@@ -211,10 +211,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt",
     )
 
     parser.add_argument(
@@ -675,12 +675,14 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
+    # Load id of the <blk> token and the vocab size
     # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
+    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)