use token_table instead

egs/librispeech/ASR/zipformer/onnx_decode.py

@@ -64,6 +64,7 @@ It will generate the following 3 files inside $repo/exp:
   --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
   --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
   --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
 """
 
 
@@ -73,7 +74,6 @@ import time
 from pathlib import Path
 from typing import List, Tuple
 
-import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
@@ -81,6 +81,7 @@ from asr_datamodule import LibriSpeechAsrDataModule
 from onnx_pretrained import greedy_search, OnnxModel
 
 from icefall.utils import setup_logger, store_transcripts, write_error_stats
+from k2 import SymbolTable
 
 
 def get_parser():
@@ -117,10 +118,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        help="""Path to tokens.txt.""",
     )
 
     parser.add_argument(
@@ -134,7 +134,7 @@ def get_parser():
 
 
 def decode_one_batch(
-    model: OnnxModel, sp: spm.SentencePieceProcessor, batch: dict
+    model: OnnxModel, token_table: SymbolTable, batch: dict
 ) -> List[List[str]]:
     """Decode one batch and return the result.
     Currently it only greedy_search is supported.
@@ -142,8 +142,8 @@ def decode_one_batch(
     Args:
       model:
         The neural model.
-      sp:
-        The BPE model.
+      token_table:
+        The token table.
       batch:
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
@@ -165,14 +165,20 @@ def decode_one_batch(
         model=model, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens
     )
 
-    hyps = [sp.decode(h).split() for h in hyps]
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += token_table[i]
+        return text.replace("▁", " ").strip()
+
+    hyps = [token_ids_to_words(h).split() for h in hyps]
     return hyps
 
 
 def decode_dataset(
     dl: torch.utils.data.DataLoader,
     model: nn.Module,
-    sp: spm.SentencePieceProcessor,
+    token_table: SymbolTable,
 ) -> Tuple[List[Tuple[str, List[str], List[str]]], float]:
     """Decode dataset.
 
@@ -181,8 +187,8 @@ def decode_dataset(
         PyTorch's dataloader containing the dataset to decode.
       model:
         The neural model.
-      sp:
-        The BPE model.
+      token_table:
+        The token table.
 
     Returns:
       - A list of tuples. Each tuple contains three elements:
@@ -207,7 +213,7 @@ def decode_dataset(
         cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
         total_duration += sum([cut.duration for cut in batch["supervisions"]["cut"]])
 
-        hyps = decode_one_batch(model=model, sp=sp, batch=batch)
+        hyps = decode_one_batch(model=model, token_table=token_table, batch=batch)
 
         this_batch = []
         assert len(hyps) == len(texts)
@@ -271,11 +277,7 @@ def main():
     device = torch.device("cpu")
     logging.info(f"Device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(args.bpe_model)
-
-    blank_id = sp.piece_to_id("<blk>")
-    assert blank_id == 0, blank_id
+    token_table = SymbolTable.from_file(args.tokens)
 
     logging.info(vars(args))
 
@@ -301,7 +303,7 @@ def main():
 
     for test_set, test_dl in zip(test_sets, test_dl):
         start_time = time.time()
-        results, total_duration = decode_dataset(dl=test_dl, model=model, sp=sp)
+        results, total_duration = decode_dataset(dl=test_dl, model=model, token_table=token_table)
         end_time = time.time()
         elapsed_seconds = end_time - start_time
         rtf = elapsed_seconds / total_duration