Learn how to evaluate speaker diarization systems using DER, WDER, and JER metrics, benchmark datasets, and real-world evaluation strategies.

Discover what speaker diarization is, how it works, and why it’s essential for modern AI and speech applications. Learn how pyannote turns multi-speaker audio into structured, intelligent data.

Deploy Open-Source diarization models on pyannoteAI: Production-Ready speaker diarization without the infrastructure overhead

Community-1: Unleashing open-source diarization

Introducing Precision-2: more accurate diarization, better speaker counting, improved timestamps/cross-talk, speaker control, STT reconciliation, confidence scores.

Together with Argmax, we are bringing pyannoteAI’s most accurate speaker diarization models on-device, running locally.