WeKws

Roadmap | Paper

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.

Typical Scenario

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

Installation

• 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 wekws python=3.8
conda activate wekws
pip install -r requirements.txt
conda install pytorch=1.10.0 torchaudio=0.10.0 cudatoolkit=11.1 -c pytorch -c conda-forge

Dataset

We plan to support a variaty of open source wake-up word datasets, include but not limited to:

• Hey Snips
• Google Speech Command
• Hi Miya(你好米雅)
• Hi Xiaowen(你好小问)

All the well-trained models on these dataset will be made public avaliable.

Runtime

We plan to support a variaty of hardwares and platforms, including:

• Web browser
• x86
• Android
• Raspberry Pi

Discussion

For Chinese users, you can scan the QR code on the left to follow our offical account of WeNet. We also created a WeChat group for better discussion and quicker response. Please scan the QR code on the right to join the chat group.

Reference

• Mining Effective Negative Training Samples for Keyword Spotting (github, paper)
• Max-pooling Loss Training of Long Short-term Memory Networks for Small-footprint Keyword Spotting (paper)
• A depthwise separable convolutional neural network for keyword spotting on an embedded system (github, paper)
• Hello Edge: Keyword Spotting on Microcontrollers (github, paper)
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling (github, paper)