diff --git a/demos/streaming_tts_serving_fastdeploy/README.md b/demos/streaming_tts_serving_fastdeploy/README.md
index 5990b18da..3e983a06d 100644
--- a/demos/streaming_tts_serving_fastdeploy/README.md
+++ b/demos/streaming_tts_serving_fastdeploy/README.md
@@ -7,54 +7,61 @@ This demo is an implementation of starting the streaming speech synthesis servic
`Server` must be started in the docker, while `Client` does not have to be in the docker.
-We assume your model and code (which will be loaded by the `Server`) absolute path in your host is `$PWD` and the model absolute path in docker is `/models`
+**The streaming_tts_serving under the path of this article ($PWD) contains the configuration and code of the model, which needs to be mapped to the docker for use.**
## Usage
### 1. Server
#### 1.1 Docker
-`docker pull registry.baidubce.com/paddlepaddle/fastdeploy_serving_cpu_only:22.09`
-
-`docker run -dit --net=host --name fastdeploy --shm-size="1g" -v $PWD:/models registry.baidubce.com/paddlepaddle/fastdeploy_serving_cpu_only:22.09`
-
-`docker exec -it -u root fastdeploy bash`
+```bash
+docker pull registry.baidubce.com/paddlepaddle/fastdeploy_serving_cpu_only:22.09
+docker run -dit --net=host --name fastdeploy --shm-size="1g" -v $PWD:/models registry.baidubce.com/paddlepaddle/fastdeploy_serving_cpu_only:22.09
+docker exec -it -u root fastdeploy bash
+```
#### 1.2 Installation(inside the docker)
-
-`apt-get install build-essential python3-dev libssl-dev libffi-dev libxml2 libxml2-dev libxslt1-dev zlib1g-dev libsndfile1 language-pack-zh-hans wget zip`
-
-`pip3 install paddlespeech`
-
-`export LC_ALL="zh_CN.UTF-8"`
-
-`export LANG="zh_CN.UTF-8"`
-
-`export LANGUAGE="zh_CN:zh:en_US:en"`
+```bash
+apt-get install build-essential python3-dev libssl-dev libffi-dev libxml2 libxml2-dev libxslt1-dev zlib1g-dev libsndfile1 language-pack-zh-hans wget zip
+pip3 install paddlespeech
+export LC_ALL="zh_CN.UTF-8"
+export LANG="zh_CN.UTF-8"
+export LANGUAGE="zh_CN:zh:en_US:en"
+```
#### 1.3 Download models(inside the docker)
-
-`cd /models/streaming_tts_serving/1`
-
-`wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip`
-
-`wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip`
-
-`unzip fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip`
-
-`unzip mb_melgan_csmsc_onnx_0.2.0.zip`
+```bash
+cd /models/streaming_tts_serving/1
+wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip
+wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip
+unzip fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip
+unzip mb_melgan_csmsc_onnx_0.2.0.zip
+```
+**For the convenience of users, we recommend that you use the command `docker -v` to map $PWD (streaming_tts_service and the configuration and code of the model contained therein) to the docker path `/models`. You can also use other methods, but regardless of which method you use, the final model directory and structure in the docker are shown in the following figure.**
+
+
+ 
+
#### 1.4 Start the server(inside the docker)
-`fastdeployserver --model-repository=/models --model-control-mode=explicit --load-model=streaming_tts_serving`
-
-**The default port is 8000(for http),8001(for grpc),8002(for metrics). If you want to change the port, add the command `--http-port 9000 --grpc-port 9001 --metrics-port 9002`**
+```bash
+fastdeployserver --model-repository=/models --model-control-mode=explicit --load-model=streaming_tts_serving
+```
+Arguments:
+ - `model-repository`(required): Path of model storage.
+ - `model-control-mode`(required): The mode of loading the model. At present, you can use 'explicit'.
+ - `load-model`(required): Name of the model to be loaded.
+ - `http-port`(optional): Port for http service. Default: `8000`. This is not used in our example.
+ - `grpc-port`(optional): Port for grpc service. Default: `8001`.
+ - `metrics-port`(optional): Port for metrics service. Default: `8002`. This is not used in our example.
### 2. Client
#### 2.1 Installation
-
-`pip3 install tritonclient[all]`
+```bash
+pip3 install tritonclient[all]
+```
#### 2.2 Send request
-
-`python3 /models/streaming_tts_serving/stream_client.py`
-
+```bash
+python3 /models/streaming_tts_serving/stream_client.py
+```
diff --git a/demos/streaming_tts_serving_fastdeploy/README_cn.md b/demos/streaming_tts_serving_fastdeploy/README_cn.md
index 92019040b..7edd32830 100644
--- a/demos/streaming_tts_serving_fastdeploy/README_cn.md
+++ b/demos/streaming_tts_serving_fastdeploy/README_cn.md
@@ -3,58 +3,65 @@
# 流式语音合成服务
## 介绍
+
本文介绍了使用FastDeploy搭建流式语音合成服务的方法。
`服务端`必须在docker内启动,而`客户端`不是必须在docker容器内.
-我们假设你的模型和代码(`服务端`会加载模型和代码以启动服务)在你主机上的绝对路径是`$PWD`,模型和代码在docker内的绝对路径是`/models`
+**本文所在路径`($PWD)下的streaming_tts_serving里包含模型的配置和代码`(服务端会加载模型和代码以启动服务),需要将其映射到docker中使用。**
## 使用
### 1. 服务端
#### 1.1 Docker
-
-`docker pull registry.baidubce.com/paddlepaddle/fastdeploy_serving_cpu_only:22.09`
-
-`docker run -dit --net=host --name fastdeploy --shm-size="1g" -v $PWD:/models registry.baidubce.com/paddlepaddle/fastdeploy_serving_cpu_only:22.09`
-
-`docker exec -it -u root fastdeploy bash`
+```bash
+docker pull registry.baidubce.com/paddlepaddle/fastdeploy_serving_cpu_only:22.09
+docker run -dit --net=host --name fastdeploy --shm-size="1g" -v $PWD:/models registry.baidubce.com/paddlepaddle/fastdeploy_serving_cpu_only:22.09
+docker exec -it -u root fastdeploy bash
+```
#### 1.2 安装(在docker内)
-
-`apt-get install build-essential python3-dev libssl-dev libffi-dev libxml2 libxml2-dev libxslt1-dev zlib1g-dev libsndfile1 language-pack-zh-hans wget zip`
-
-`pip3 install paddlespeech`
-
-`export LC_ALL="zh_CN.UTF-8"`
-
-`export LANG="zh_CN.UTF-8"`
-
-`export LANGUAGE="zh_CN:zh:en_US:en"`
+```bash
+apt-get install build-essential python3-dev libssl-dev libffi-dev libxml2 libxml2-dev libxslt1-dev zlib1g-dev libsndfile1 language-pack-zh-hans wget zip
+pip3 install paddlespeech
+export LC_ALL="zh_CN.UTF-8"
+export LANG="zh_CN.UTF-8"
+export LANGUAGE="zh_CN:zh:en_US:en"
+```
#### 1.3 下载模型(在docker内)
-
-`cd /models/streaming_tts_serving/1`
-
-`wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip`
-
-`wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip`
-
-`unzip fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip`
-
-`unzip mb_melgan_csmsc_onnx_0.2.0.zip`
+```bash
+cd /models/streaming_tts_serving/1
+wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip
+wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip
+unzip fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip
+unzip mb_melgan_csmsc_onnx_0.2.0.zip
+```
+**为了方便用户使用，我们推荐用户使用1.1中的`docker -v`命令将`$PWD(streaming_tts_serving及里面包含的模型的配置和代码)映射到了docker内的/models路径`,用户也可以使用其他办法,但无论使用哪种方法,最终在docker内的模型目录及结构如下图所示。**
+
+
+
+
#### 1.4 启动服务端(在docker内)
-
-`fastdeployserver --model-repository=/models --model-control-mode=explicit --load-model=streaming_tts_serving`
-
-**服务启动的默认端口是8000(for http),8001(for grpc),8002(for metrics). 如果想要改变服务的端口号,在上述命令后面添加以下参数即可`--http-port 9000 --grpc-port 9001 --metrics-port 9002`**
+```bash
+fastdeployserver --model-repository=/models --model-control-mode=explicit --load-model=streaming_tts_serving
+```
+
+参数:
+ - `model-repository`(required): 整套模型streaming_tts_serving存放的路径.
+ - `model-control-mode`(required): 模型加载的方式,现阶段, 使用'explicit'即可.
+ - `load-model`(required): 需要加载的模型的名称.
+ - `http-port`(optional): HTTP服务的端口号. 默认: `8000`. 本示例中未使用该端口.
+ - `grpc-port`(optional): GRPC服务的端口号. 默认: `8001`.
+ - `metrics-port`(optional): 服务端指标的端口号. 默认: `8002`. 本示例中未使用该端口.
### 2. 客户端
#### 2.1 安装
-
-`pip3 install tritonclient[all]`
+```bash
+pip3 install tritonclient[all]
+```
#### 2.2 发送请求
-
-`python3 /models/streaming_tts_serving/stream_client.py`
-
+```bash
+python3 /models/streaming_tts_serving/stream_client.py
+```
diff --git a/demos/streaming_tts_serving_fastdeploy/streaming_tts_serving/1/model.py b/demos/streaming_tts_serving_fastdeploy/streaming_tts_serving/1/model.py
index f165bf287..46473fdb2 100644
--- a/demos/streaming_tts_serving_fastdeploy/streaming_tts_serving/1/model.py
+++ b/demos/streaming_tts_serving_fastdeploy/streaming_tts_serving/1/model.py
@@ -20,7 +20,7 @@ am_pad = 12
voc_upsample = 300
# 模型路径
-dir_name = "/models/streaming_tts_serving/1"
+dir_name = "/models/streaming_tts_serving/1/"
phones_dict = dir_name + "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/phone_id_map.txt"
am_stat_path = dir_name + "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/speech_stats.npy"
diff --git a/demos/streaming_tts_serving_fastdeploy/streaming_tts_serving/stream_client.py b/demos/streaming_tts_serving_fastdeploy/streaming_tts_serving/stream_client.py
index 0d36eaaf9..e7f120b7d 100644
--- a/demos/streaming_tts_serving_fastdeploy/streaming_tts_serving/stream_client.py
+++ b/demos/streaming_tts_serving_fastdeploy/streaming_tts_serving/stream_client.py
@@ -74,7 +74,6 @@ if __name__ == '__main__':
values = ["哈哈哈哈"]
request_id = "0"
- #string_sequence_id0 = str(uuid.uuid4())
string_result0_list = []
@@ -111,7 +110,7 @@ if __name__ == '__main__':
status = data_item.as_numpy('status')
print('sub_wav = ', sub_wav, "subwav.shape = ", sub_wav.shape)
print('status = ', status)
- if status[0] is True:
+ if status[0] == 1:
break
recv_count += 1