update repo

funasr/build_utils/build_asr_model.py

@@ -239,6 +239,7 @@ def build_asr_model(args):
         vocab_size = len(token_list)
         logging.info(f"Vocabulary size: {vocab_size}")
     else:
+        token_list = None
         vocab_size = None
 
     # frontend
@@ -265,7 +266,10 @@ def build_asr_model(args):
     # normalization layer
     if args.normalize is not None:
         normalize_class = normalize_choices.get_class(args.normalize)
-        normalize = normalize_class(**args.normalize_conf)
+        if args.model == "mfcca":
+            normalize = normalize_class(stats_file=args.cmvn_file,**args.normalize_conf)
+        else:
+            normalize = normalize_class(**args.normalize_conf)
     else:
         normalize = None
 
@@ -300,7 +304,7 @@ def build_asr_model(args):
             **args.model_conf,
         )
     elif args.model in ["paraformer", "paraformer_online", "paraformer_bert", "bicif_paraformer",
-                        "contextual_paraformer"]:
+                        "contextual_paraformer", "neatcontextual_paraformer"]:
         # predictor
         predictor_class = predictor_choices.get_class(args.predictor)
         predictor = predictor_class(**args.predictor_conf)

funasr/models/e2e_uni_asr.py

@@ -50,9 +50,7 @@ class UniASR(FunASRModel):
         frontend: Optional[AbsFrontend],
         specaug: Optional[AbsSpecAug],
         normalize: Optional[AbsNormalize],
-        preencoder: Optional[AbsPreEncoder],
         encoder: AbsEncoder,
-        postencoder: Optional[AbsPostEncoder],
         decoder: AbsDecoder,
         ctc: CTC,
         ctc_weight: float = 0.5,
@@ -80,6 +78,8 @@ class UniASR(FunASRModel):
         loss_weight_model1: float = 0.5,
         enable_maas_finetune: bool = False,
         freeze_encoder2: bool = False,
+        preencoder: Optional[AbsPreEncoder] = None,
+        postencoder: Optional[AbsPostEncoder] = None,
         encoder1_encoder2_joint_training: bool = True,
     ):
         assert check_argument_types()