Production First and Production Ready End-to-End Keyword Spotting Toolkit.
The goal of this toolkit it to…
Small footprint keyword spotting (KWS), or specifically wake-up word (WuW) detection is a typical and important module in internet of things (IoT) devices. It provides a way for users to control IoT devices with a hands-free experience. A WuW detection system usually runs locally and persistently on IoT devices, which requires low consumptional power, less model parameters, low computational comlexity and to detect predefined keyword in a streaming way, i.e., requires low latency.
We are going to support the following typical applications of wakeup word:
- Single wake-up word
- Multiple wake-up words
- Customizable wake-up word
- Personalized wake-up word, i.e. combination of wake-up word detection and voiceprint
- Clone the repo
git clone https://github.com/wenet-e2e/wekws.git
- Install Conda: please see https://docs.conda.io/en/latest/miniconda.html
- Create Conda env:
conda create -n wenet python=3.8 conda activate wenet pip install -r requirements.txt conda install pytorch=1.10.0 torchaudio=0.10.0 cudatoolkit=11.1 -c pytorch -c conda-forge
We plan to support a variaty of open source wake-up word datasets, include but not limited to:
All the well-trained models on these dataset will be made public avaliable.
We plan to support a variaty of hardwares and platforms, including:
- Web browser
- Raspberry Pi