The big advantage is that Assembly AI handles the particularly horrible problem you spotted today,

matching speaker boundaries to words. Universal 3.5 Pro now does per-word speaker attribution,

rather than you independently generating whisper segments and piano segments and trying to

reconcile them afterwards. That is probably why its output looks noticeably cleaner,

but it costs you $64 a month. Option B, whisper NVIDIA NEMO TITANET. This costs essentially

nothing beyond electricity. You'd have whisper, transcription and NEMO diarization,

speaker ABC with TITANET providing the speaker representations and NEMO's diarization machinery

doing the segmentation clustering. Then, TITANET embeddings, QDRAM, actual identity. That could

potentially replace piano completely. And because TITANET is small, I would expect this to be

substantially less demanding than the combination you're presently running. You still have the

whisper diarization timestamp alignment problem, though. We'd need to make sure BRI does word

level alignment, rather than simply assigning whole whisper chunks to whichever diarized

speaker overlaps the most.

Option C, whisper NEMO TITANET. This costs essentially nothing beyond electricity,

but it costs you $64 a month. Option C, keep piano diarization.