revise from_pretrained function

deepspeech/exps/deepspeech2/bin/deploy/runtime.py

@@ -29,6 +29,9 @@ from deepspeech.utils.socket_server import warm_up_test
 from deepspeech.utils.utility import add_arguments
 from deepspeech.utils.utility import print_arguments
 
+from paddle.io import DataLoader
+from deepspeech.io.collator import SpeechCollator
+
 
 def init_predictor(args):
     if args.model_dir is not None:
@@ -83,7 +86,12 @@ def start_server(config, args):
     config.data.keep_transcription_text = True
     dataset = ManifestDataset.from_config(config)
 
-    model = DeepSpeech2Model.from_pretrained(dataset, config,
+    config.collator.batch_size=1
+    config.collator.num_workers=0
+    collate_fn = SpeechCollator.from_config(config)
+    test_loader = DataLoader(dataset_dataset, collate_fn=collate_fn, num_workers=0)
+
+    model = DeepSpeech2Model.from_pretrained(test_loader, config,
                                              args.checkpoint_path)
     model.eval()
 

deepspeech/exps/deepspeech2/bin/deploy/server.py

@@ -28,6 +28,9 @@ from deepspeech.utils.utility import add_arguments
 from deepspeech.utils.utility import print_arguments
 
 
+from paddle.io import DataLoader
+from deepspeech.io.collator import SpeechCollator
+
 def start_server(config, args):
     """Start the ASR server"""
     config.defrost()
@@ -36,7 +39,12 @@ def start_server(config, args):
     config.data.keep_transcription_text = True
     dataset = ManifestDataset.from_config(config)
 
-    model = DeepSpeech2Model.from_pretrained(dataset, config,
+    config.collator.batch_size=1
+    config.collator.num_workers=0
+    collate_fn = SpeechCollator.from_config(config)
+    test_loader = DataLoader(dataset_dataset, collate_fn=collate_fn, num_workers=0)
+
+    model = DeepSpeech2Model.from_pretrained(test_loader, config,
                                              args.checkpoint_path)
     model.eval()
 

deepspeech/exps/deepspeech2/bin/tune.py

@@ -47,7 +47,7 @@ def tune(config, args):
         drop_last=False,
         collate_fn=SpeechCollator(keep_transcription_text=True))
 
-    model = DeepSpeech2Model.from_pretrained(dev_dataset, config,
+    model = DeepSpeech2Model.from_pretrained(valid_loader, config,
                                              args.checkpoint_path)
     model.eval()
 

deepspeech/exps/deepspeech2/model.py

@@ -318,7 +318,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
 
     def export(self):
         infer_model = DeepSpeech2InferModel.from_pretrained(
-            self.test_loader.dataset, self.config, self.args.checkpoint_path)
+            self.test_loader, self.config, self.args.checkpoint_path)
         infer_model.eval()
         feat_dim = self.test_loader.collate_fn.feature_size
         static_model = paddle.jit.to_static(

deepspeech/exps/u2/model.py

@@ -506,7 +506,7 @@ class U2Tester(U2Trainer):
             List[paddle.static.InputSpec]: input spec.
         """
         from deepspeech.models.u2 import U2InferModel
-        infer_model = U2InferModel.from_pretrained(self.test_loader.dataset,
+        infer_model = U2InferModel.from_pretrained(self.test_loader,
                                                    self.config.model.clone(),
                                                    self.args.checkpoint_path)
         feat_dim = self.test_loader.collate_fn.feature_size

deepspeech/models/deepspeech2.py

@@ -198,11 +198,11 @@ class DeepSpeech2Model(nn.Layer):
             cutoff_top_n, num_processes)
 
     @classmethod
-    def from_pretrained(cls, dataset, config, checkpoint_path):
+    def from_pretrained(cls, dataloader, config, checkpoint_path):
         """Build a DeepSpeech2Model model from a pretrained model.
         Parameters
         ----------
-        dataset: paddle.io.Dataset
+        dataloader: paddle.io.DataLoader
 
         config: yacs.config.CfgNode
             model configs
@@ -215,8 +215,8 @@ class DeepSpeech2Model(nn.Layer):
         DeepSpeech2Model
             The model built from pretrained result.
         """
-        model = cls(feat_size=dataset.feature_size,
+        model = cls(feat_size=dataloader.collate_fn.feature_size,
-                    dict_size=dataset.vocab_size,
+                    dict_size=dataloader.collate_fn.vocab_size,
                     num_conv_layers=config.model.num_conv_layers,
                     num_rnn_layers=config.model.num_rnn_layers,
                     rnn_size=config.model.rnn_layer_size,

deepspeech/models/u2.py

@@ -876,11 +876,11 @@ class U2Model(U2BaseModel):
         return model
 
     @classmethod
-    def from_pretrained(cls, dataset, config, checkpoint_path):
+    def from_pretrained(cls, dataloader, config, checkpoint_path):
         """Build a DeepSpeech2Model model from a pretrained model.
 
         Args:
-            dataset (paddle.io.Dataset): not used.
+            dataloader (paddle.io.DataLoader): not used.
             config (yacs.config.CfgNode):  model configs
             checkpoint_path (Path or str): the path of pretrained model checkpoint, without extension name
 
@@ -888,8 +888,8 @@ class U2Model(U2BaseModel):
             DeepSpeech2Model: The model built from pretrained result.
         """
         config.defrost()
-        config.input_dim = dataset.feature_size
+        config.input_dim = dataloader.collate_fn.feature_size
-        config.output_dim = dataset.vocab_size
+        config.output_dim = dataloader.collate_fn.vocab_size
         config.freeze()
         model = cls.from_config(config)