Does Whisper do speaker diarization? Whisper + pyannote, and its limits

Whisper is an automatic speech recognition model that transcribes audio but does not identify speakers, so users commonly combine it with pyannote.audio or WhisperX through a three-stage process of transcription, diarization, and timestamp-based word-to-speaker alignment. While this open-source approach can suit offline, research, or hobby projects, it requires gated-model credentials, GPU infrastructure, voice-activity detection tuning, and custom alignment logic, and can struggle with short interjections, overlapping speech, and speaker merging. The piece argues that concatenated minimum-permutation word error rate (cpWER), which measures transcription and attribution errors per speaker, better reflects practical diarization quality than diarization error rate (DER), which may understate errors in brief or overlapping turns. It presents AssemblyAI’s Universal-3.5 Pro as a managed alternative that jointly generates transcripts and speaker labels through an API, claiming lower average cpWER than several competitors, simpler setup, and per-hour pricing, while acknowledging that it is not appropriate for offline or air-gapped use.

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