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* Add threads to transcribe_v2.pyyum2023-09-03
| | | | | | | | | | | | Four threads: * Main thread * Transcription (mic -> collector -> whisper -> committer -> pager) * VR input * Keyboard input Also: * add OscPager class to encapsulate all OSC interactions. * bump `last_n_must_match` from 2 to 3 to reduce hallucinations
* Apply subtle compression to audio before transcribingyum2023-09-03
| | | | This has a slight positive effect on my benchmark.
* Experiment with Collector filtersyum2023-09-03
| | | | | | | | | | | | | | | | | | | | | | Try adding two filters on top of the usual AudioCollector: * Minimum length preservation: never report fewer than N seconds worth of audio data. Pad with silence as needed. * Volume normalizing: normalize audio volume. Using my benchmark of 30-second audio clips from 3 speakers (lower is better): length enf + norm = 87.118 nothing = 90.917 norm = 94.538 length = 111.402 Both together are a slight improvement, but independently degrade the result by a lot. I also observed more hallucinations in a conversational pattern when using them vs. not. So I'll phase them out. I'm still curious about *compression* as opposed to normalization.
* Begin rewriting transcribe.pyyum2023-09-02
A set of proper interfaces is called for. See #dev-update-spam in discord for drawing of design. Also add code to mechanically optimize committer parameters using an audio file. Not perfectly repeatable since it depends on the performance characteristics of the machine, but prob better than what we had before (nothing).