would make their eye dramatically more intelligent. Contextual speaker identity

should supplement the voice match, not replace it. Today's recording, for example,

gives powerful non-acoustic evidence. You know the recorder belongs to you. David

is discussed and addressed directly. Damoon is explicitly named. There are only a

handful of plausible people. The preceding location meeting context

identifies the participants. If the acoustic recognizer says, open quote,

Damoon 0.71, unknown 0.29, close quote, and the transcript says

another speaker addresses him as Damoon, I ought to be able to promote that

identification. Likewise on a phone call, one side can very often be identified

from the telephone number, meaning only the other voice needs recognition. That

combination, excellent vendor diarization plus persistent local voice embeddings

plus contextual identity evidence, is, I think, a much better architecture than

merely lowering the current Piano to Q-drant cosine threshold. And at 23 cents

per recorded hour, I'd now seriously consider making Assembly AI your

standard transcription backend and keeping the

G-drama as a standard.

The CPU whisper pipeline is a fallback such archive process