pruned-transducer-stateless2-for-wenetspeech

egs/wenetspeech/ASR/pruned_transducer_stateless2/decode.py

@@ -63,10 +63,9 @@ from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
 import k2
-import sentencepiece as spm
 import torch
 import torch.nn as nn
-from asr_datamodule import LibriSpeechAsrDataModule
+from asr_datamodule import WenetSpeechAsrDataModule
 from beam_search import (
     beam_search,
     fast_beam_search,
@@ -81,6 +80,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
+from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
     setup_logger,
@@ -203,7 +203,7 @@ def get_parser():
 def decode_one_batch(
     params: AttributeDict,
     model: nn.Module,
-    sp: spm.SentencePieceProcessor,
+    lexicon: Lexicon,
     batch: dict,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[List[str]]]:
@@ -222,8 +222,6 @@ def decode_one_batch(
         It's the return value of :func:`get_params`.
       model:
         The neural model.
-      sp:
-        The BPE model.
       batch:
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
@@ -260,8 +258,8 @@ def decode_one_batch(
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-        for hyp in sp.decode(hyp_tokens):
+        for i in range(encoder_out.size(0)):
-            hyps.append(hyp.split())
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens])
     elif (
         params.decoding_method == "greedy_search"
         and params.max_sym_per_frame == 1
@@ -270,16 +268,16 @@ def decode_one_batch(
             model=model,
             encoder_out=encoder_out,
         )
-        for hyp in sp.decode(hyp_tokens):
+        for i in range(encoder_out.size(0)):
-            hyps.append(hyp.split())
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens])
     elif params.decoding_method == "modified_beam_search":
         hyp_tokens = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
             beam=params.beam_size,
         )
-        for hyp in sp.decode(hyp_tokens):
+        for i in range(encoder_out.size(0)):
-            hyps.append(hyp.split())
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens])
     else:
         batch_size = encoder_out.size(0)
 
@@ -303,7 +301,7 @@ def decode_one_batch(
                 raise ValueError(
                     f"Unsupported decoding method: {params.decoding_method}"
                 )
-            hyps.append(sp.decode(hyp).split())
+            hyps.append([lexicon.token_table[idx] for idx in hyp])
 
     if params.decoding_method == "greedy_search":
         return {"greedy_search": hyps}
@@ -323,7 +321,7 @@ def decode_dataset(
     dl: torch.utils.data.DataLoader,
     params: AttributeDict,
     model: nn.Module,
-    sp: spm.SentencePieceProcessor,
+    lexicon: Lexicon,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
@@ -335,8 +333,6 @@ def decode_dataset(
         It is returned by :func:`get_params`.
       model:
         The neural model.
-      sp:
-        The BPE model.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
         only when --decoding_method is fast_beam_search.
@@ -366,7 +362,7 @@ def decode_dataset(
         hyps_dict = decode_one_batch(
             params=params,
             model=model,
-            sp=sp,
+            lexicon=lexicon,
             decoding_graph=decoding_graph,
             batch=batch,
         )
@@ -438,7 +434,7 @@ def save_results(
 @torch.no_grad()
 def main():
     parser = get_parser()
-    LibriSpeechAsrDataModule.add_arguments(parser)
+    WenetSpeechAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
 
@@ -473,12 +469,9 @@ def main():
 
     logging.info(f"Device: {device}")
 
-    sp = spm.SentencePieceProcessor()
+    lexicon = Lexicon(params.lang_dir)
-    sp.load(params.bpe_model)
+    params.blank_id = lexicon.token_table["<blk>"]
-
+    params.vocab_size = max(lexicon.tokens) + 1
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
 
     logging.info(params)
 
@@ -514,26 +507,24 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    librispeech = LibriSpeechAsrDataModule(args)
+    wenetspeech = WenetSpeechAsrDataModule(args)
+    test_net_cuts = wenetspeech.test_net_cuts()
+    test_meeting_cuts = wenetspeech.test_meeting_cuts()
 
-    test_clean_cuts = librispeech.test_clean_cuts()
+    test_net_dl = wenetspeech.valid_dataloaders(test_net_cuts)
-    test_other_cuts = librispeech.test_other_cuts()
+    test_meeting_dl = wenetspeech.test_dataloaders(test_meeting_cuts)
 
-    test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
+    test_sets = ["TEST_NET", "TEST_MEETING"]
-    test_other_dl = librispeech.test_dataloaders(test_other_cuts)
+    test_dl = [test_net_dl, test_meeting_dl]
-
-    test_sets = ["test-clean", "test-other"]
-    test_dl = [test_clean_dl, test_other_dl]
 
     for test_set, test_dl in zip(test_sets, test_dl):
         results_dict = decode_dataset(
             dl=test_dl,
             params=params,
             model=model,
-            sp=sp,
+            lexicon=lexicon,
             decoding_graph=decoding_graph,
         )
-
         save_results(
             params=params,
             test_set_name=test_set,