FastDeploy/examples/audio/pp-tts/serving/README.md

[简体中文](README_CN.md) | English

# PP-TTS Streaming Text-to-Speech Serving

## Introduction
This demo is an implementation of starting the streaming speech synthesis service and accessing the service.

`Server` must be started in the docker, while `Client` does not have to be in the docker.

**The streaming_pp_tts 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

```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)
```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
python3 -m pip install --upgrade pip
pip3 install -U fastdeploy-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
pip3 install -U paddlespeech paddlepaddle
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, skippable)
The model file will be downloaded and decompressed automatically. If you want to download manually, please use the following command.
```bash
cd /models/streaming_pp_tts/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_pp_tts 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.**

```
/models 
│
└───streaming_pp_tts                                                #Directory of the entire service model
    │   config.pbtxt                                                #Configuration file of service model
    │   stream_client.py                                            #Code of Client
    │
    └───1                                                           #Model version number
        │   model.py                                                #Code to start the model
        └───fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0       #Model file required by code
        └───mb_melgan_csmsc_onnx_0.2.0                              #Model file required by code

```

#### 1.4 Start the server (inside the docker)

```bash
fastdeployserver --model-repository=/models --model-control-mode=explicit --load-model=streaming_pp_tts
```
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
```bash
pip3 install tritonclient[all]
```

#### 2.2 Send request
```bash
python3 /models/streaming_pp_tts/stream_client.py
```