Snowman Hotword Detection
Snowman Hotword Detection is an open source rewrite of the popular Snowboy library originally developed by Kitt.AI. It was created in the hope of preserving support for and improving it, as well as allowing it to be used on modern devices (embedded and desktop).
While I did my best, reversing software tends to be more art than science, so it is very likely that I introduced some bugs while doing so. The fact that I have very little experience with audio processing and neural networks does not really help either. If you have either of those and could spare some time proofreading what I did, that would be highly appreciated. I won't provide any warranty for it, but I did my best to make sure this library won't light your PC on fire.
In the default build configuration, it should be a drop-in replacement for the original snowboy library. However, it does not implement everything the original library did. The most important differences are the following:
Missing frontend processing: I do not implement any automatic gain control or noise suppression (both were part of the library if you enabled "ApplyFrontend"), so make sure you have a good audio source until it is implemented. Voice Activity Detection (VAD) does work, however.
Missing support for some hotword search algorithms: There are multiple hotword search algorithms used by universal models. I have only implemented "Naive" so far and added asserts to those that are completely unused and redirected used ones to the Naive method, which seems to work fine. However, we should probably implement all of them at some point.
Split Radix FFT: There were two supported FFT modes, normal FFT and split radix FFT. So far I have only implemented normal FFT and hardcoded SRFFT models to use normal FFT. From my understanding, the result should be identical, but split radix FFT might have better performance.
PipelineVAD: While reversed, it is totally untested. That said, most of the code is identical with PipelineDetect and thus somewhat tested, so I don't expect any major bugs in it.
Wave reading, PipelineNNETForward: While present in the executable, they where never exposed with headers so no user code should rely on them. I might implement them at some point, though.
Existing universal models should work out of the box and perform similarly to the original library. Since they are designed to work with "ApplyFrontend" disabled, the missing AGC/NoiseSuppression should not have an effect.
New universal models should be doable in theory. However, I don't know enough about neural networks to do so. If you do, please reach out to me. Another issue is the lack of a way to gather samples. In the future I might build a website similar to the original kitt.ai website where people can train their personal models using a nice UI, as well as an option to share audio samples for building universal models, but this is still in the far future.
Training personal models is now possible using the
enroll utility build along the library. While the resulting model is not bit identical with models trained using the original library, it is identical to 5 digits of precision. The remaining differences are most likely a result of rounding errors within the process and should not affect the performance of the model.
As before the main interface is
snowboy-detect.h which includes the well known
snowboy::SnowboyTemplateCut classes. Those classes provide a very high level interface to snowboy that should be sufficient for most applications. There is also a file
snowboy-detect-c.h file which provides a C wrapper for the beforementioned classes and should make integration into other languages a lot easier.
Any help would be highly appreciated. I am particularly looking for people with knowledge of machine learning, audio processing or reverse engineering. However, all help is welcome. Simply take a look at the open issues and pull requests, as well as the TODO.md file.
The original project was licensed under the Apache 2 license, so this one is as well. Just please don't use it as a voice interface for Skynet.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License at
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