PaddleSpeech/demos/keyword_spotting/README.md

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KWS (Keyword Spotting)

Introduction

KWS(Keyword Spotting) is a technique to recognize keyword from a giving speech audio.

This demo is an implementation to recognize keyword from a specific audio file. It can be done by a single command or a few lines in python using PaddleSpeech.

Usage

1. Installation

see installation.

You can choose one way from easy, meduim and hard to install paddlespeech.

2. Prepare Input File

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

Here are sample files for this demo that can be downloaded:

wget -c https://paddlespeech.bj.bcebos.com/kws/hey_snips.wav https://paddlespeech.bj.bcebos.com/kws/non-keyword.wav

3. Usage

• Command Line(Recommended)

  paddlespeech kws --input ./hey_snips.wav
  paddlespeech kws --input ./non-keyword.wav
  

  Usage:

  paddlespeech kws --help
  

  Arguments:

  • input(required): Audio file to recognize.
  • threshold：Score threshold for kws. Default: 0.8.
  • model: Model type of kws task. Default: mdtc_heysnips.
  • config: Config of kws task. Use pretrained model when it is None. Default: None.
  • ckpt_path: Model checkpoint. Use pretrained model when it is None. Default: None.
  • device: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.
  • verbose: Show the log information.

  Output:

  # Input file: ./hey_snips.wav
  Score: 1.000, Threshold: 0.8, Is keyword: True
  # Input file: ./non-keyword.wav
  Score: 0.000, Threshold: 0.8, Is keyword: False
  

• Python API

  import paddle
  from paddlespeech.cli.kws import KWSExecutor
  
  kws_executor = KWSExecutor()
  result = kws_executor(
      audio_file='./hey_snips.wav',
      threshold=0.8,
      model='mdtc_heysnips',
      config=None,
      ckpt_path=None,
      device=paddle.get_device())
  print('KWS Result: \n{}'.format(result))
  

  Output:

  KWS Result:
  Score: 1.000, Threshold: 0.8, Is keyword: True
  

4.Pretrained Models

Here is a list of pretrained models released by PaddleSpeech that can be used by command and python API:

Model	Language	Sample Rate
mdtc_heysnips	en	16k