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PaddleSpeech/demos/speech_server
lym0302 434708cff4
set device cpu, test=doc
3 years ago
..
conf set device cpu, test=doc 3 years ago
README.md remove wavfile, test=doc 3 years ago
README_cn.md remove wavfile, test=doc 3 years ago
asr_client.sh remove wavfile, test=doc 3 years ago
server.sh add server demo, test=doc 3 years ago
tts_client.sh add server demo, test=doc 3 years ago

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.

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.

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

  • 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

  • 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.
    [2022-02-23 15:20:37,875] [    INFO] - RTF: 0.096346
    
    
    
  • 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.
    RTF: 0.107493
    
    

Pretrained Models

ASR model

Here is a list of ASR pretrained models released by PaddleSpeech, both command line and python interfaces are available:

Model Language Sample Rate
conformer_wenetspeech zh 16000
transformer_librispeech en 16000

TTS model

Here is a list of TTS pretrained models released by PaddleSpeech, both command line and python interfaces are available:

  • Acoustic model

    Model Language
    speedyspeech_csmsc zh
    fastspeech2_csmsc zh
    fastspeech2_aishell3 zh
    fastspeech2_ljspeech en
    fastspeech2_vctk en
  • Vocoder

    Model Language
    pwgan_csmsc zh
    pwgan_aishell3 zh
    pwgan_ljspeech en
    pwgan_vctk en
    mb_melgan_csmsc zh

Here is a list of TTS pretrained static models released by PaddleSpeech, both command line and python interfaces are available:

  • Acoustic model

    Model Language
    speedyspeech_csmsc zh
    fastspeech2_csmsc zh
  • Vocoder

    Model Language
    pwgan_csmsc zh
    mb_melgan_csmsc zh
    hifigan_csmsc zh