sync transcription, diarization, expected speaker count range, speaker identification context

proceedings, word timing, relatively tiny cost, and you already remember its output being

substantially better. Deepgram would be my second experiment, particularly because its per-word

speaker assignment is architecturally attractive. But I wouldn't throw Pianote away. This is where

I think a hybrid becomes interesting. You could use assembly AI, authoritative words, plus

diarization, and then your local speaker embedding model, persistent identity. So assembly AI

determines, open quote, speaker A said words X, Y, speaker B said words Z, Q, close quote, dot,

and BRI subsequently asks, open quote, who is speaker A, close quote, using accumulated audio

for that whole recording speaker against Q-draft. That is much cleaner than asking Pianote and

Whisper independently where the boundaries are and trying to glue their incompatible time

together afterwards. You could even retain Pianote purely as the speaker embedding generator if

its embeddings prove good enough, while no longer using its diarization segmentation as the

canonical transcript structure. There is one further thing I'd like to say about this.

I'd build which I