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([简体中文](./README_cn.md)|English)
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# KWS (Keyword Spotting)
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## Introduction
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KWS(Keyword Spotting) is a technique to recognize keyword from a giving speech audio.
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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`.
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## Usage
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### 1. Installation
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see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
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You can choose one way from easy, meduim and hard to install paddlespeech.
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### 2. Prepare Input File
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The input of this demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.
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Here are sample files for this demo that can be downloaded:
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```bash
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wget -c https://paddlespeech.bj.bcebos.com/kws/hey_snips.wav https://paddlespeech.bj.bcebos.com/kws/non-keyword.wav
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```
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### 3. Usage
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- Command Line(Recommended)
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```bash
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paddlespeech kws --input ./hey_snips.wav
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paddlespeech kws --input ./non-keyword.wav
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```
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Usage:
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```bash
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paddlespeech kws --help
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```
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Arguments:
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- `input`(required): Audio file to recognize.
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- `threshold`：Score threshold for kws. Default: `0.8`.
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- `model`: Model type of kws task. Default: `mdtc_heysnips`.
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- `config`: Config of kws task. Use pretrained model when it is None. Default: `None`.
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- `ckpt_path`: Model checkpoint. Use pretrained model when it is None. Default: `None`.
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- `device`: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.
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- `verbose`: Show the log information.
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Output:
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```bash
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# Input file: ./hey_snips.wav
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Score: 1.000, Threshold: 0.8, Is keyword: True
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# Input file: ./non-keyword.wav
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Score: 0.000, Threshold: 0.8, Is keyword: False
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```
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- Python API
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```python
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import paddle
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from paddlespeech.cli.kws import KWSExecutor
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kws_executor = KWSExecutor()
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result = kws_executor(
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audio_file='./hey_snips.wav',
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threshold=0.8,
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model='mdtc_heysnips',
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config=None,
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ckpt_path=None,
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device=paddle.get_device())
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print('KWS Result: \n{}'.format(result))
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```
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Output:
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```bash
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KWS Result:
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Score: 1.000, Threshold: 0.8, Is keyword: True
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```
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### 4.Pretrained Models
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Here is a list of pretrained models released by PaddleSpeech that can be used by command and python API:
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| Model | Language | Sample Rate
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| :--- | :---: | :---: |
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| mdtc_heysnips | en | 16k
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