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 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-forgeWe 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
- x86
- Android
- Raspberry Pi
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.
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- 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)