diff --git a/demos/streaming_asr_server/conf/ws_conformer_application.yaml b/demos/streaming_asr_server/conf/ws_conformer_application.yaml
index 2affde07..6a10741b 100644
--- a/demos/streaming_asr_server/conf/ws_conformer_application.yaml
+++ b/demos/streaming_asr_server/conf/ws_conformer_application.yaml
@@ -4,7 +4,7 @@
 #                             SERVER SETTING                                    #
 #################################################################################
 host: 0.0.0.0
-port: 8090
+port: 8091
 
 # The task format in the engin_list is: <speech task>_<engine type>
 # task choices = ['asr_online']
diff --git a/demos/streaming_asr_server/conf/ws_application.yaml b/demos/streaming_asr_server/conf/ws_ds2_application.yaml
similarity index 100%
rename from demos/streaming_asr_server/conf/ws_application.yaml
rename to demos/streaming_asr_server/conf/ws_ds2_application.yaml
diff --git a/paddlespeech/server/engine/asr/online/asr_engine.py b/paddlespeech/server/engine/asr/online/asr_engine.py
index 70bfcfb6..d7bd458f 100644
--- a/paddlespeech/server/engine/asr/online/asr_engine.py
+++ b/paddlespeech/server/engine/asr/online/asr_engine.py
@@ -53,7 +53,7 @@ class PaddleASRConnectionHanddler:
         logger.info(
             "create an paddle asr connection handler to process the websocket connection"
         )
-        self.config = asr_engine.config
+        self.config = asr_engine.config # server config
         self.model_config = asr_engine.executor.config
         self.asr_engine = asr_engine
 
@@ -249,10 +249,13 @@ class PaddleASRConnectionHanddler:
     def reset(self):
         if "deepspeech2" in self.model_type:
             # for deepspeech2 
-            self.chunk_state_h_box = copy.deepcopy(
-                self.asr_engine.executor.chunk_state_h_box)
-            self.chunk_state_c_box = copy.deepcopy(
-                self.asr_engine.executor.chunk_state_c_box)
+            # init state
+            self.chunk_state_h_box = np.zeros(
+                (self.model_config .num_rnn_layers, 1, self.model_config.rnn_layer_size),
+                dtype=float32)
+            self.chunk_state_c_box = np.zeros(
+                (self.model_config.num_rnn_layers, 1, self.model_config.rnn_layer_size),
+                dtype=float32)
             self.decoder.reset_decoder(batch_size=1)
 
         self.device = None
@@ -803,36 +806,6 @@ class ASRServerExecutor(ASRExecutor):
                 model_file=self.am_model,
                 params_file=self.am_params,
                 predictor_conf=self.am_predictor_conf)
-
-            # decoder
-            logger.info("ASR engine start to create the ctc decoder instance")
-            self.decoder = CTCDecoder(
-                odim=self.config.output_dim,  # <blank> is in  vocab
-                enc_n_units=self.config.rnn_layer_size * 2,
-                blank_id=self.config.blank_id,
-                dropout_rate=0.0,
-                reduction=True,  # sum
-                batch_average=True,  # sum / batch_size
-                grad_norm_type=self.config.get('ctc_grad_norm_type', None))
-
-            # init decoder
-            logger.info("ASR engine start to init the ctc decoder")
-            cfg = self.config.decode
-            decode_batch_size = 1  # for online
-            self.decoder.init_decoder(
-                decode_batch_size, self.text_feature.vocab_list,
-                cfg.decoding_method, cfg.lang_model_path, cfg.alpha, cfg.beta,
-                cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n,
-                cfg.num_proc_bsearch)
-
-            # init state box
-            self.chunk_state_h_box = np.zeros(
-                (self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
-                dtype=float32)
-            self.chunk_state_c_box = np.zeros(
-                (self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
-                dtype=float32)
-
         elif "conformer" in model_type or "transformer" in model_type:
             model_name = model_type[:model_type.rindex(
                 '_')]  # model_type: {model_name}_{dataset}
@@ -847,15 +820,11 @@ class ASRServerExecutor(ASRExecutor):
             model_dict = paddle.load(self.am_model)
             self.model.set_state_dict(model_dict)
             logger.info("create the transformer like model success")
-
-            # update the ctc decoding
-            self.searcher = CTCPrefixBeamSearch(self.config.decode)
-            self.transformer_decode_reset()
         else:
             raise ValueError(f"Not support: {model_type}")
 
         return True
-
+        
 
 class ASREngine(BaseEngine):
     """ASR server resource
@@ -881,8 +850,8 @@ class ASREngine(BaseEngine):
         self.executor = ASRServerExecutor()
 
         try:
-            default_dev = paddle.get_device()
-            paddle.set_device(self.config.get("device", default_dev))
+            self.device = self.config.get("device", paddle.get_device())
+            paddle.set_device(self.device)
         except BaseException as e:
             logger.error(
                 f"Set device failed, please check if device '{self.device}' is already used and the parameter 'device' in the yaml file"