revert init_model.py and add init_model in export_jit

wenet/paraformer/ali_paraformer/export_jit.py

@@ -4,8 +4,12 @@
 import argparse
 import torch
 import yaml
+from wenet.cif.predictor import Predictor
+from wenet.paraformer.ali_paraformer.model import (AliParaformer, SanmDecoer,
+                                                   SanmEncoder)
+from wenet.transformer.cmvn import GlobalCMVN
 from wenet.utils.checkpoint import load_checkpoint
-from wenet.utils.init_model import init_model
+from wenet.utils.cmvn import load_cmvn
 
 
 def get_args():
@@ -23,6 +27,28 @@ def get_args():
     return args
 
 
+def init_model(configs):
+    mean, istd = load_cmvn(configs['cmvn_file'], configs['is_json_cmvn'])
+    global_cmvn = GlobalCMVN(
+        torch.from_numpy(mean).float(),
+        torch.from_numpy(istd).float())
+    input_dim = configs['input_dim']
+    vocab_size = configs['output_dim']
+    encoder = SanmEncoder(global_cmvn=global_cmvn,
+                          input_size=configs['lfr_conf']['lfr_m'] * input_dim,
+                          **configs['encoder_conf'])
+    decoder = decoder = SanmDecoer(vocab_size=vocab_size,
+                                   encoder_output_size=encoder.output_size(),
+                                   **configs['decoder_conf'])
+    predictor = Predictor(**configs['cif_predictor_conf'])
+    model = AliParaformer(
+        encoder=encoder,
+        decoder=decoder,
+        predictor=predictor,
+    )
+    return model
+
+
 def main():
 
     args = get_args()

wenet/utils/init_model.py

@@ -14,9 +14,7 @@
 
 import torch
 
-from wenet.paraformer.ali_paraformer.model import SanmDecoer, SanmEncoder
 from wenet.k2.model import K2Model
-
 from wenet.transducer.joint import TransducerJoint
 from wenet.transducer.predictor import (ConvPredictor, EmbeddingPredictor,
                                         RNNPredictor)
@@ -31,7 +29,6 @@
 from wenet.squeezeformer.encoder import SqueezeformerEncoder
 from wenet.efficient_conformer.encoder import EfficientConformerEncoder
 from wenet.paraformer.paraformer import Paraformer
-from wenet.paraformer.ali_paraformer.model import AliParaformer
 from wenet.cif.predictor import Predictor
 from wenet.utils.cmvn import load_cmvn
 
@@ -74,24 +71,13 @@ def init_model(configs):
         encoder = EBranchformerEncoder(input_dim,
                                        global_cmvn=global_cmvn,
                                        **configs['encoder_conf'])
-    elif encoder_type == 'SanmEncoder':
-        assert 'lfr_conf' in configs
-        encoder = SanmEncoder(global_cmvn=global_cmvn,
-                              input_size=configs['lfr_conf']['lfr_m'] *
-                              input_dim,
-                              **configs['encoder_conf'])
     else:
         encoder = TransformerEncoder(input_dim,
                                      global_cmvn=global_cmvn,
                                      **configs['encoder_conf'])
     if decoder_type == 'transformer':
         decoder = TransformerDecoder(vocab_size, encoder.output_size(),
                                      **configs['decoder_conf'])
-    elif decoder_type == 'SanmDecoder':
-        assert isinstance(encoder, SanmEncoder)
-        decoder = SanmDecoer(vocab_size=vocab_size,
-                             encoder_output_size=encoder.output_size(),
-                             **configs['decoder_conf'])
     else:
         assert 0.0 < configs['model_conf']['reverse_weight'] < 1.0
         assert configs['decoder_conf']['r_num_blocks'] > 0
@@ -131,21 +117,12 @@ def init_model(configs):
                            **configs['model_conf'])
     elif 'paraformer' in configs:
         predictor = Predictor(**configs['cif_predictor_conf'])
-        if isinstance(encoder, SanmEncoder):
-            assert isinstance(decoder, SanmDecoer)
-            # NOTE(Mddct): only support inference for now
-            model = AliParaformer(
-                encoder=encoder,
-                decoder=decoder,
-                predictor=predictor,
-            )
-        else:
-            model = Paraformer(vocab_size=vocab_size,
-                               encoder=encoder,
-                               decoder=decoder,
-                               ctc=ctc,
-                               predictor=predictor,
-                               **configs['model_conf'])
+        model = Paraformer(vocab_size=vocab_size,
+                           encoder=encoder,
+                           decoder=decoder,
+                           ctc=ctc,
+                           predictor=predictor,
+                           **configs['model_conf'])
     else:
         print(configs)
         if configs.get('lfmmi_dir', '') != '':