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PaddleSpeech/demos/keyword_spotting
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Add kws cli and demo.
2 years ago
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README.md Add kws cli and demo. 2 years ago
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README.md

(简体中文|English)

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.
    • thresholdScore 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