modify server demo, test=doc

demos/speech_server/README.md

@@ -182,6 +182,52 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
 
   ```
 
+### 6. CLS Client Usage
+**Note:** The response time will be slightly longer when using the client for the first time
+- Command Line (Recommended)
+   ```
+   paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
+   ```
+
+  Usage:
+  
+  ```bash
+  paddlespeech_client cls --help
+  ```
+  Arguments:
+  - `server_ip`: server ip. Default: 127.0.0.1
+  - `port`: server port. Default: 8090
+  - `input`(required): Audio file to be classified.
+  - `topk`: topk scores of classification result.
+
+  Output:
+  ```bash
+  [2022-03-09 20:44:39,974] [    INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}}
+  [2022-03-09 20:44:39,975] [    INFO] - Response time 0.104360 s.
+
+
+  ```
+
+- Python API
+  ```python
+  from paddlespeech.server.bin.paddlespeech_client import CLSClientExecutor
+
+  clsclient_executor = CLSClientExecutor()
+  clsclient_executor(
+      input="./zh.wav",
+      server_ip="127.0.0.1",
+      port=8090,
+      topk=1)
+
+  ```
+
+  Output:
+  ```bash
+  {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}}
+  Response time 0.150897 s.
+
+  ```
+
 
 ## Models supported by the service
 ### ASR model
@@ -189,3 +235,6 @@ Get all models supported by the ASR service via `paddlespeech_server stats --tas
 
 ### TTS model
 Get all models supported by the TTS service via `paddlespeech_server stats --task tts`, where static models can be used for paddle inference inference.
+
+### CLS model
+Get all models supported by the CLS service via `paddlespeech_server stats --task cls`, where static models can be used for paddle inference inference.

demos/speech_server/README_cn.md

@@ -80,7 +80,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
 
   ```
 
-### 4. ASR客户端使用方法
+### 4. ASR 客户端使用方法
 **注意：** 初次使用客户端时响应时间会略长
 - 命令行 (推荐使用)
    ```
@@ -129,7 +129,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
 
   ```
  
-### 5. TTS客户端使用方法
+### 5. TTS 客户端使用方法
 **注意：** 初次使用客户端时响应时间会略长
 - 命令行 (推荐使用)
 
@@ -185,9 +185,59 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
 
   ```
 
+  ### 5. CLS 客户端使用方法
+  **注意：** 初次使用客户端时响应时间会略长
+  - 命令行 (推荐使用)
+   ```
+   paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
+   ```
+
+  使用帮助:
+  
+  ```bash
+  paddlespeech_client cls --help
+  ```
+  参数:
+  - `server_ip`: 服务端ip地址，默认: 127.0.0.1。
+  - `port`: 服务端口，默认: 8090。
+  - `input`(必须输入): 用于分类的音频文件。
+  - `topk`: 分类结果的topk。
+
+  输出:
+  ```bash
+  [2022-03-09 20:44:39,974] [    INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}}
+  [2022-03-09 20:44:39,975] [    INFO] - Response time 0.104360 s.
+
+
+  ```
+
+- Python API
+  ```python
+  from paddlespeech.server.bin.paddlespeech_client import CLSClientExecutor
+
+  clsclient_executor = CLSClientExecutor()
+  clsclient_executor(
+      input="./zh.wav",
+      server_ip="127.0.0.1",
+      port=8090,
+      topk=1)
+
+  ```
+
+  输出:
+  ```bash
+  {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}}
+  Response time 0.150897 s.
+
+  ```
+
+
 ## 服务支持的模型
 ### ASR支持的模型
 通过 `paddlespeech_server stats --task asr` 获取ASR服务支持的所有模型，其中静态模型可用于 paddle inference 推理。 
 
 ### TTS支持的模型
 通过 `paddlespeech_server stats --task tts` 获取TTS服务支持的所有模型，其中静态模型可用于 paddle inference 推理。
+
+### CLS支持的模型
+通过 `paddlespeech_server stats --task cls` 获取CLS服务支持的所有模型，其中静态模型可用于 paddle inference 推理。

paddlespeech/server/bin/paddlespeech_server.py

@@ -103,13 +103,14 @@ class ServerStatsExecutor():
             '--task',
             type=str,
             default=None,
-            choices=['asr', 'tts'],
+            choices=['asr', 'tts', 'cls'],
             help='Choose speech task.',
             required=True)
-        self.task_choices = ['asr', 'tts']
+        self.task_choices = ['asr', 'tts', 'cls']
         self.model_name_format = {
             'asr': 'Model-Language-Sample Rate',
-            'tts': 'Model-Language'
+            'tts': 'Model-Language',
+            'cls': 'Model-Sample Rate'
         }
 
     def show_support_models(self, pretrained_models: dict):
@@ -173,54 +174,25 @@ class ServerStatsExecutor():
                     "Failed to get the table of TTS pretrained models supported in the service."
                 )
                 return False
-
-    @stats_wrapper
-    def __call__(
-            self,
-            task: str=None, ):
-        """
-            Python API to call an executor.
-        """
-        self.task = task
-        if self.task not in self.task_choices:
-            print("Please input correct speech task, choices = ['asr', 'tts']")
-
-        elif self.task == 'asr':
+        
+        elif self.task == 'cls':
             try:
-                from paddlespeech.cli.asr.infer import pretrained_models
-                print(
-                    "Here is the table of ASR pretrained models supported in the service."
-                )
-                self.show_support_models(pretrained_models)
-
-                # show ASR static pretrained model
-                from paddlespeech.server.engine.asr.paddleinference.asr_engine import pretrained_models
-                print(
-                    "Here is the table of ASR static pretrained models supported in the service."
-                )
-                self.show_support_models(pretrained_models)
-
-            except BaseException:
-                print(
-                    "Failed to get the table of ASR pretrained models supported in the service."
-                )
-
-        elif self.task == 'tts':
-            try:
-                from paddlespeech.cli.tts.infer import pretrained_models
-                print(
-                    "Here is the table of TTS pretrained models supported in the service."
+                from paddlespeech.cli.cls.infer import pretrained_models
+                logger.info(
+                    "Here is the table of CLS pretrained models supported in the service."
                 )
                 self.show_support_models(pretrained_models)
 
-                # show TTS static pretrained model
-                from paddlespeech.server.engine.tts.paddleinference.tts_engine import pretrained_models
-                print(
-                    "Here is the table of TTS static pretrained models supported in the service."
+                # show CLS static pretrained model
+                from paddlespeech.server.engine.cls.paddleinference.cls_engine import pretrained_models
+                logger.info(
+                    "Here is the table of CLS static pretrained models supported in the service."
                 )
                 self.show_support_models(pretrained_models)
 
+                return True
             except BaseException:
-                print(
-                    "Failed to get the table of TTS pretrained models supported in the service."
+                logger.error(
+                    "Failed to get the table of CLS pretrained models supported in the service."
                 )
+                return False