Speaker diarization is a technology that automatically separates and labels voices in an audio stream, making it easier to understand conversations with multiple speakers. It adds structure to unstructured audio, providing metadata for further analysis or transcription. The key applications of speaker diarization include meeting transcription and summarization, call center analytics, broadcast media processing, podcast and audiobook indexing, courtroom proceedings, and healthcare session monitoring. Speaker diarization is essential for making audio-driven systems more intelligent, personal, and practical, as it enables better speech recognition, conversational AI, and content organization. The evaluation of speaker diarization systems uses metrics like Diarization Error Rate (DER), Jaccard Error Rate (JER), and Word-Level Diarization Error Rate (WDER). Encord is a comprehensive multimodal AI data platform that facilitates efficient management and annotation of large-scale unstructured datasets, including audio files, for speaker diarization.