Speaker diarization is the process of automatically splitting audio or video inputs based on speaker identity, answering the question "who spoke when?". With advancements in deep learning, automatic speaker verification and identification with confidence has become possible. Industries like media monitoring, telephony, podcasting, telemedicine, and web conferencing rely on speaker diarization to replace human transcription from their workflows. The process involves speech detection, segmentation, embedding extraction, and clustering. Speaker diarization can be enabled with AssemblyAI by submitting an audio or video file for transcription with Speaker Labels turned on. Use cases include telemedicine, conference calls, podcast hosting, hiring platforms, video hosting, and broadcast media.