PaddleSpeech/demos/streaming_tts_serving_fastdeploy

(简体中文|English)

Streaming Speech Synthesis Service

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_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

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"

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

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

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]

2.2 Send request

python3 /models/streaming_tts_serving/stream_client.py