Foley-Omni: A Unified Multimodal Audio Generation Model to Automatically Generate Complete Soundtracks for Videos

Foley-Omni is an open-source multimodal audio generation model that supports generating speech, sound effects, and music based on text descriptions and video content, realizing end-to-end video soundtrack synthesis. This project aims to solve the time-consuming and professional problems of traditional video audio production through a unified model architecture, lowering the threshold for audio production.

In the field of video content creation, audio production is time-consuming and requires professional skills. The traditional process needs to handle speech, sound effects, and background music separately, involving multiple tools and professional knowledge. With the development of multimodal large model technology, researchers have explored the possibility of combining visual understanding with audio generation, leading to the emergence of Foley-Omni. It attempts to simultaneously handle three tasks—speech synthesis, sound effect generation, and music creation—through a unified model architecture, providing a complete automatic soundtrack solution.

Foley-Omni adopts an end-to-end multimodal design:

1. Unified Conditional Input Mechanism: Supports text conditions (natural language descriptions of audio attributes) and video conditions (analyzing frames to generate synchronized audio);
2. Triple Audio Generation Capability: Integrates speech synthesis (multiple tones/intonations), sound effect generation (environmental sounds/action sounds, etc.), and music creation (background music matching emotions);
3. Two Usage Modes: Task-level synthesis (fine-grained control over specific audio types) and complete soundtrack synthesis (generating a full soundtrack including speech, sound effects, and music in one go, automatically handling layers and timing).

Foley-Omni's application scenarios include:

• Video Content Creation: Lowers the audio production threshold for short video creators and independent filmmakers;
• Game Development: Quickly generates prototype sound effects and background music, supporting procedural audio;
• Accessible Content Production: Automatically generates narration speech and environmental sound effects to improve content accessibility;
• AI-Assisted Creation Workflow: Cooperates with video generation models to realize end-to-end text-to-full audio-visual content generation.

Foley-Omni is implemented based on Python with a code size of approximately 71KB and adopts a modular design. The model architecture is presumed to include a visual encoder (extracting video features), a text encoder (processing natural language conditions), a multimodal fusion module, an audio decoder (diffusion or autoregressive model), and a timing alignment mechanism. As a GitHub open-source project (currently with 4 stars and 1 fork), although it is in the early stage, its unified architecture concept has reference value for the multimodal audio generation field.

Developers trying this project should note:

1. Hardware Requirements: A high-performance GPU is recommended;
2. Dependency Environment: Check the Python version and deep learning framework versions;
3. License Agreement: Carefully read the open-source license terms;
4. Community Participation: The project is in active development; you can participate in its construction via issues and PRs.

Foley-Omni is an important attempt in AI audio generation towards the multimodal and end-to-end direction. By handling three audio types with a unified model and supporting dual-modal input, it provides a new path for automatic video soundtracking. In the future, with the progress of multimodal large model technology, more similar open-source tools are expected to emerge, further lowering the threshold for audio-visual production and allowing creators to focus more on content creativity.