The Speaker Diarization API enables accurate segmentation of audio recordings by detecting and labeling individual speakers across time. Designed for seamless integration into transcription pipelines, media workflows, and audio analytics systems, it supports a wide range of formats including WAV, MP3, FLAC, and OGG. The service is language-agnostic and works across diverse audio sourcecalls, meetings, interviews, podcasts, and more. With built-in support for mono and stereo channels, varying sample rates, and flexible input options it can be deployed in batch or near-real-time use cases. Key features include automatic speaker count estimation, precise time-stamped speaker labeling, and detection of overlapping speech. Outputs are returned in structured JSON for easy integration with transcription engines, search indexes, or business intelligence tools. Whether you are enriching speech-to-text transcripts, analyzing call center performance, or processing long-form media, this API improves clarity, organization, and data usability.