Local speaker diarization on a MacBook Air M3 runs 4.4x faster on the GPU

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The author transcribes meetings and interviews on a MacBook Air M3 using whisper.cpp for speech recognition and pyannote.audio for speaker diarization, keeping audio off third-party services for privacy reasons. An initial run of a 42-minute meeting took about 2 hours 9 minutes and heavily loaded the laptop. Process monitoring showed pyannote.audio running on the CPU at around 343% usage, because it was never explicitly assigned to the GPU, even though whisper.cpp already used Metal. The author moved processing into a background job launched with nohup and changed the device setup, achieving a 4.4x speedup for diarization and a 2.5x speedup for the whole pipeline. The changes were not measured individually, and the initial baseline was not a controlled comparison.

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