PaddleSpeech/demos/speech_server

(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