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README.md

(简体中文|English)

Speech Server

Introduction

This demo is an implementation of starting the voice service and accessing the service. It can be achieved with a single command using paddlespeech_server and paddlespeech_client or a few lines of code in python.

Usage

1. Installation

see installation.

It is recommended to use paddlepaddle 2.2.1 or above. You can choose one way from easy, meduim and hard to install paddlespeech.

2. Prepare config File

The configuration file contains the service-related configuration files and the model configuration related to the voice tasks contained in the service. They are all under the conf folder.

Note: The configuration of engine_backend in application.yaml represents all speech tasks included in the started service. If the service you want to start contains only a certain speech task, then you need to comment out the speech tasks that do not need to be included. For example, if you only want to use the speech recognition (ASR) service, then you can comment out the speech synthesis (TTS) service, as in the following example:

engine_backend:
    asr: 'conf/asr/asr.yaml'
    #tts: 'conf/tts/tts.yaml'

Note: The configuration file of engine_backend in application.yaml needs to match the configuration type of engine_type. When the configuration file of engine_backend is XXX.yaml, the configuration type of engine_type needs to be set to python; when the configuration file of engine_backend is XXX_pd.yaml, the configuration of engine_type needs to be set type is inference;

The input of ASR client demo should be a WAV file(.wav), and the sample rate must be the same as the model.

Here are sample files for thisASR client demo that can be downloaded:

wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav

3. Server Usage

Command Line (Recommended)

# start the service
paddlespeech_server start --config_file ./conf/application.yaml

Usage:

paddlespeech_server start --help

Arguments:

config_file: yaml file of the app, defalut: ./conf/application.yaml
log_file: log file. Default: ./log/paddlespeech.log

Output:

[2022-02-23 11:17:32] [INFO] [server.py:64] Started server process [6384]
INFO:     Waiting for application startup.
[2022-02-23 11:17:32] [INFO] [on.py:26] Waiting for application startup.
INFO:     Application startup complete.
[2022-02-23 11:17:32] [INFO] [on.py:38] Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
[2022-02-23 11:17:32] [INFO] [server.py:204] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)

Python API

from paddlespeech.server.bin.paddlespeech_server import ServerExecutor

server_executor = ServerExecutor()
server_executor(
    config_file="./conf/application.yaml", 
    log_file="./log/paddlespeech.log")

Output:

INFO:     Started server process [529]
[2022-02-23 14:57:56] [INFO] [server.py:64] Started server process [529]
INFO:     Waiting for application startup.
[2022-02-23 14:57:56] [INFO] [on.py:26] Waiting for application startup.
INFO:     Application startup complete.
[2022-02-23 14:57:56] [INFO] [on.py:38] Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
[2022-02-23 14:57:56] [INFO] [server.py:204] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)

4. ASR Client Usage

Note: The response time will be slightly longer when using the client for the first time

Command Line (Recommended)

paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav

Usage:

paddlespeech_client asr --help

Arguments:

server_ip: server ip. Default: 127.0.0.1
port: server port. Default: 8090
input(required): Audio file to be recognized.
sample_rate: Audio ampling rate, default: 16000.
lang: Language. Default: "zh_cn".
audio_format: Audio format. Default: "wav".

Output:

[2022-02-23 18:11:22,819] [    INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'transcription': '我认为跑步最重要的就是给我带来了身体健康'}}
[2022-02-23 18:11:22,820] [    INFO] - time cost 0.689145 s.

Python API

from paddlespeech.server.bin.paddlespeech_client import ASRClientExecutor

asrclient_executor = ASRClientExecutor()
asrclient_executor(
    input="./zh.wav",
    server_ip="127.0.0.1",
    port=8090,
    sample_rate=16000,
    lang="zh_cn",
    audio_format="wav")

Output:

{'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'transcription': '我认为跑步最重要的就是给我带来了身体健康'}}
time cost 0.604353 s.

5. TTS Client Usage

Note: The response time will be slightly longer when using the client for the first time

Command Line (Recommended)
```
paddlespeech_client tts --server_ip 127.0.0.1 --port 8090 --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
```
Usage:
```
paddlespeech_client tts --help
```
Arguments:
- server_ip: server ip. Default: 127.0.0.1
- port: server port. Default: 8090
- input(required): Input text to generate.
- spk_id: Speaker id for multi-speaker text to speech. Default: 0
- speed: Audio speed, the value should be set between 0 and 3. Default: 1.0
- volume: Audio volume, the value should be set between 0 and 3. Default: 1.0
- sample_rate: Sampling rate, choice: [0, 8000, 16000], the default is the same as the model. Default: 0
- output: Output wave filepath. Default: output.wav.
Output:
```
[2022-02-23 15:20:37,875] [    INFO] - {'description': 'success.'}
[2022-02-23 15:20:37,875] [    INFO] - Save synthesized audio successfully on output.wav.
[2022-02-23 15:20:37,875] [    INFO] - Audio duration: 3.612500 s.
[2022-02-23 15:20:37,875] [    INFO] - Response time: 0.348050 s.
```

Python API

from paddlespeech.server.bin.paddlespeech_client import TTSClientExecutor

ttsclient_executor = TTSClientExecutor()
ttsclient_executor(
    input="您好，欢迎使用百度飞桨语音合成服务。",
    server_ip="127.0.0.1",
    port=8090,
    spk_id=0,
    speed=1.0,
    volume=1.0,
    sample_rate=0,
    output="./output.wav")

Output:

{'description': 'success.'}
Save synthesized audio successfully on ./output.wav.
Audio duration: 3.612500 s.
Response time: 0.388317 s.

Models supported by the service

ASR model

Get all models supported by the ASR service via paddlespeech_server stats --task asr, where static models can be used for paddle inference inference.

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.