diff --git a/paddlespeech/cli/asr/infer.py b/paddlespeech/cli/asr/infer.py
index 3ff5be12e29f3c5d81635caf63851a103efe5ff0..30e4bb9c1a81282a9cbf639f9e6638f274462f32 100644
--- a/paddlespeech/cli/asr/infer.py
+++ b/paddlespeech/cli/asr/infer.py
@@ -137,6 +137,10 @@ class ASRExecutor(BaseExecutor):
         """
             Init model and other resources from a specific path.
         """
+        if hasattr(self, 'model'):
+            logger.info('Model had been initialized.')
+            return
+
         if cfg_path is None or ckpt_path is None:
             sample_rate_str = '16k' if sample_rate == 16000 else '8k'
             tag = model_type + '_' + lang + '_' + sample_rate_str
diff --git a/paddlespeech/cli/cls/infer.py b/paddlespeech/cli/cls/infer.py
index 5b5894fa37881b7a117ce017bea0857b9e164089..c4206f7e5e9b0e33c3f0b4259557ee234f01b9ce 100644
--- a/paddlespeech/cli/cls/infer.py
+++ b/paddlespeech/cli/cls/infer.py
@@ -128,6 +128,10 @@ class CLSExecutor(BaseExecutor):
         """
             Init model and other resources from a specific path.
         """
+        if hasattr(self, 'model'):
+            logger.info('Model had been initialized.')
+            return
+
         if label_file is None or ckpt_path is None:
             self.res_path = self._get_pretrained_path(model_type)  # panns_cnn14
             self.cfg_path = os.path.join(
@@ -154,9 +158,9 @@ class CLSExecutor(BaseExecutor):
         # model
         model_class = dynamic_import(model_type, model_alias)
         model_dict = paddle.load(self.ckpt_path)
-        self._model = model_class(extract_embedding=False)
-        self._model.set_state_dict(model_dict)
-        self._model.eval()
+        self.model = model_class(extract_embedding=False)
+        self.model.set_state_dict(model_dict)
+        self.model.eval()
 
     def preprocess(self, audio_file: Union[str, os.PathLike]):
         """
@@ -192,7 +196,7 @@ class CLSExecutor(BaseExecutor):
         """
             Model inference and result stored in self.output.
         """
-        self._outputs['logits'] = self._model(self._inputs['feats'])
+        self._outputs['logits'] = self.model(self._inputs['feats'])
 
     def _generate_topk_label(self, result: np.ndarray, topk: int) -> str:
         assert topk <= len(