Audiobox by Meta

Audiobox is a unified AI audio generation research demo developed by Meta's Fundamental AI Research (FAIR) lab that explores the capability to generate multiple categories of audio—speech, natural sounds, and soundscapes—from text descriptions and optional voice prompts within a single model architecture. Users can provide a text description of desired audio content and, for speech generation, a reference voice recording to guide the output's vocal characteristics. The system aims to demonstrate that a single audio model can address speech synthesis, sound effect generation, and ambient audio production rather than requiring separate specialized models for each category. Audiobox is primarily of interest to AI researchers exploring audio generation models, audio engineers and sound designers who want to experiment with text-to-audio generation for sound effects and environments, and creative technologists testing AI-generated voice and audio for multimedia projects. A typical experimental workflow involves writing a descriptive text prompt for the desired audio output, optionally providing a voice reference for speech synthesis, and generating the audio through the demo interface. Browse tools. Podcast creators, game audio designers, and film production teams exploring AI audio tools have experimented with Audiobox for ambient soundscape generation and sound effect prototyping. Audiobox is a publicly available research demo rather than a production-ready commercial product, which means availability may be subject to capacity limits and the feature set reflects research priorities rather than a polished end-user tool. Voice cloning capabilities in AI audio tools carry ethical responsibilities, and users should use AI-generated voice content with the consent of individuals whose voice characteristics are being referenced. Meta publishes associated research papers for Audiobox through academic channels. As a research prototype, it should not be treated as equivalent to production audio generation platforms in terms of reliability, output consistency, or ongoing feature development See related options.