silero-vad VS kaldi-active-grammar

>How do you detect speech starting and stopping?

https://github.com/snakers4/silero-vad

you could look into https://github.com/guillaumekln/faster-whisper especially the VAD section (Voice Activity Detector) using https://github.com/snakers4/silero-vad

I also had the same synchronization issue, so I wrote a WebUI/CLI that uses Silero-VAD that first splits the audio whenever there a silent portion (or every 30 seconds), and I haven't experienced it since:

By the way, I've updated the WebUI to now also support using Silero VAD to break up the audio into distinct sections, and run Whisper on each section and then combine them into one single transcript/SRT file.

The VAD is always available on Github

I also pinned some interesting comments here regarding mobile and IOT usage here - https://github.com/snakers4/silero-vad/issues/37

https://github.com/dictation-toolbox/dragonfly

https://github.com/daanzu/kaldi-active-grammar

With an open system/engine, you can train your own personal speech model. For kaldi-active-grammar (https://github.com/daanzu/kaldi-active-grammar), you can do so without all that much difficulty, although the process/documentation could certainly use improvement.

I bootstrapped my personal speech model by retaining the commands from me using WSR. My voice is quite abnormal, and it took only 10 hours of speech data to train a model orders of magnitude more accurate than any generic model I've ever used. And of course, I retain much of my usage now with Kaldi, so my model improves more and more over time. A virtuous flywheel!

I have been coding entirely by voice for approximately 10 years now (by hand long before that). Most of that time I have been using the Dragonfly (https://github.com/dictation-toolbox/dragonfly) library to construct my own customized voice coding system. The library is highly flexible and open source, allowing you to easily customize everything to suit what you need to be productive. It is perhaps the power user analogue to Dragon Naturally Speaking. With it, you can certainly be highly productive coding by voice. In fact, I develop kaldi-active-grammar (https://github.com/daanzu/kaldi-active-grammar), a free and open source speech recognition backend usable by Dragonfly, itself entirely by voice. There's also a community of voice coders using Dragonfly and other tools that build on top of it, such as Caster (https://github.com/dictation-toolbox/Caster).

- Demo: https://www.youtube.com/watch?v=Qk1mGbIJx3s / Software: https://github.com/daanzu/kaldi-active-grammar

Far field audio is usually harder for any speech system to get correct, so having a good quality mic and using it nearby will _usually_ help with the transcription quality. As a long time Linux user, I would love to see it get some more powerful voice tools - really hope that this opens up over the next few years. Feel free to drop me an email (on my profile) happy to help with setup on any of the above.

kaldi repo: https://github.com/daanzu/kaldi-active-grammar