SAM Audio: Segment Anything in Audio
Authors: name: Bowen Shi; name: Andros Tjandra; name: John Hoffman; name: Helin Wang; name: Yi-Chiao Wu; name: Luya Gao; name: Julius Richter; name: Matt Le; name: Apoorv Vyas; name: Sanyuan Chen; name: Christoph Feichtenhofer; name: Piotr Dollár; name: Wei-Ning Hsu; name: Ann Lee
Published: 2025-12-19 22:14:23

Abstract: General audio source separation is a key capability for multimodal AI systems that can perceive and reason about sound. Despite substantial progress in recent years, existing separation models are either domain-specific, designed for fixed categories such as speech or music, or limited in controllability, supporting only a single prompting modality such as text. In this work, we present SAM Audio, a foundation model for general audio separation that unifies text, visual, and temporal span prompting within a single framework. Built on a diffusion transformer architecture, SAM Audio is trained with flow matching on large-scale audio data spanning speech, music, and general sounds, and can flexibly separate target sources described by language, visual masks, or temporal spans. The model achieves state-of-the-art performance across a diverse suite of benchmarks, including general sound, speech, music, and musical instrument separation in both in-the-wild and professionally produced audios, substantially outperforming prior general-purpose and specialized systems. Furthermore, we introduce a new real-world separation benchmark with human-labeled multimodal prompts and a reference-free evaluation model that correlates strongly with human judgment.