57 lines
1.8 KiB
Markdown
57 lines
1.8 KiB
Markdown
# wenet-kws
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Production First and Production Ready End-to-End Keyword Spotting Toolkit.
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The goal of this toolkit it to...
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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.
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## Typical Scenario
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We are going to support the following typical applications of wakeup word:
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* Single wake-up word
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* Multiple wake-up words
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* Customizable wake-up word
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* Personalized wake-up word, i.e. combination of wake-up word detection and voiceprint
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## Installation
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- Clone the repo
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``` sh
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git clone https://github.com/wenet-e2e/wenet.git
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```
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- Install Conda: please see https://docs.conda.io/en/latest/miniconda.html
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- Create Conda env:
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``` sh
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conda create -n wenet python=3.8
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conda activate wenet
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pip install -r requirements.txt
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conda install pytorch=1.10.0 torchaudio=0.10.0 cudatoolkit=11.1 -c pytorch -c conda-forge
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```
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## Dataset
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We plan to support a variaty of open source wake-up word datasets, include but not limited to:
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* [Hey Snips](https://github.com/sonos/keyword-spotting-research-datasets)
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* [Google Speech Command](https://arxiv.org/pdf/1804.03209.pdf)
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* [Hi Miya(你好米雅)](http://www.aishelltech.com/wakeup_data)
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* [Hi Xiaowen(你好小问)](http://openslr.org/87/)
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All the well-trained models on these dataset will be made public avaliable.
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## Runtime
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We plan to support a variaty of hardwares and platforms, including:
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* Web browser
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* x86
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* Android
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* Raspberry Pi
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