Combine real-time speaker diarization with any streaming ASR to get speaker-attributed transcripts. Two approaches compared, with code tutorials.

Most Voice AI pipelines miss who spoke. See how speaker-attributed audio improves transcripts, per-speaker analytics, coaching signals and compliance review.

Precision-3 cuts diarization error rate by 10.4% versus Precision-2 and 34.2% versus Community-1, but it also drastically reduces the speaker-attributed transcription word error rate (tcpWER) compared to many solutions on the market. Explore full benchmark data against commercial providers on multiple datasets.

Benchmarking ‘VAD Sensitivity’ and ‘Crosstalk Sensitivity’ across 11 DIHARD domains. Learn which settings to use when building a transcription tool, voice cloning solution, or managing a noisy audio environment.

Introducing Precision-3: 10.4% lower diarization error, new sensitivity parameters, frame-level speaker probabilities, and speed-to-accuracy operating modes.

An explainer on how dubbing and localization pipelines fail at speaker attribution rather than translation, walking through how diarization, voiceprints, and Precision-3's confidence signals supply the who-said-what metadata the rest of the chain depends on.

An explainer on why speaker diarization works across any language and code-switches without configuration, and why the transcription model you pair it with is the real constraint on multilingual pipelines.

A hands-on Python quickstart that takes you from a pyannoteAI API key to live speaker labels over a WebSocket, first streaming an audio file and then a live microphone.

A decision guide for teams choosing between streaming and batch speaker diarization, covering the test that settles it, the hybrid pattern that runs both on the same conversation, and the constraints that shape either build.