Good tips on the pre-clustering filtering- we do something similar with the 0.4s threshold on short segments, but the cosine distance merge before GMM is interesting, I'll look into that.

on the cross-session speaker consistency— yes, that's on the roadmap. The plan is to store speaker embeddings (256-dim vectors) in a vector DB and use them for matching during diarization.

Something like an anchor_embeddings parameter you can pass in, so the output labels stay consistent across calls.

Right now every call produces SPEAKER_00, SPEAKER_01 etc. independently. the embedding extraction already works well enough for matching (that's what cosine similarity on WeSpeaker embeddings is good at), the missing piece is the API surface and the matching logic on top of clustering.

What's your setup for storing/matching the centroids? Curious if you're doing it at inference time or as a post-processing step.