diff --git a/paddlespeech/server/bin/paddlespeech_server.py b/paddlespeech/server/bin/paddlespeech_server.py
index 10a91d9bef210a9a942143bd6d02028ac91d99f0..1b1792bd1753fb86af698d10666e81d04e08a69b 100644
--- a/paddlespeech/server/bin/paddlespeech_server.py
+++ b/paddlespeech/server/bin/paddlespeech_server.py
@@ -113,7 +113,7 @@ class ServerExecutor(BaseExecutor):
         """
         config = get_config(config_file)
         if self.init(config):
-            uvicorn.run(app, host=config.host, port=config.port, debug=True)
+            uvicorn.run(app, host=config.host, port=config.port)
 
 
 @cli_server_register(
diff --git a/paddlespeech/text/exps/ernie_linear/punc_restore.py b/paddlespeech/text/exps/ernie_linear/punc_restore.py
index 2cb4d07199d790ba600834c836d383f6b6f19238..98804606c5c17b2de6ae044ee2a8b56da18f741f 100644
--- a/paddlespeech/text/exps/ernie_linear/punc_restore.py
+++ b/paddlespeech/text/exps/ernie_linear/punc_restore.py
@@ -25,8 +25,6 @@ DefinedClassifier = {
     'ErnieLinear': ErnieLinear,
 }
 
-tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
-
 
 def _clean_text(text, punc_list):
     text = text.lower()
@@ -35,7 +33,7 @@ def _clean_text(text, punc_list):
     return text
 
 
-def preprocess(text, punc_list):
+def preprocess(text, punc_list, tokenizer):
     clean_text = _clean_text(text, punc_list)
     assert len(clean_text) > 0, f'Invalid input string: {text}'
     tokenized_input = tokenizer(
@@ -51,7 +49,8 @@ def test(args):
     with open(args.config) as f:
         config = CfgNode(yaml.safe_load(f))
     print("========Args========")
-    print(yaml.safe_dump(vars(args)))
+    print(yaml.safe_dump(vars(args), allow_unicode=True))
+    # print(args)
     print("========Config========")
     print(config)
 
@@ -61,10 +60,16 @@ def test(args):
             punc_list.append(line.strip())
 
     model = DefinedClassifier[config["model_type"]](**config["model"])
+    # print(model)
+
+    pretrained_token = config['data_params']['pretrained_token']
+    tokenizer = ErnieTokenizer.from_pretrained(pretrained_token)
+    # tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
+
     state_dict = paddle.load(args.checkpoint)
     model.set_state_dict(state_dict["main_params"])
     model.eval()
-    _inputs = preprocess(args.text, punc_list)
+    _inputs = preprocess(args.text, punc_list, tokenizer)
     seq_len = _inputs['seq_len']
     input_ids = paddle.to_tensor(_inputs['input_ids']).unsqueeze(0)
     seg_ids = paddle.to_tensor(_inputs['seg_ids']).unsqueeze(0)