What is ThinkSound
ThinkSound is the first CoT (Chain of Thought) audio generation model launched by Alibaba Tongyi's speech team. It is used for video dubbing, generating dedicated matching sound effects for each frame. The model introduces CoT reasoning to solve the problem that traditional technologies struggle to capture dynamic details and spatial relationships in visuals, enabling AI to think step by step like a professional sound designer and generate high-fidelity audio with synchronized picture and sound. The model drives audio generation based on a three-stage chain of thought, including basic sound effect reasoning, object-level interaction, and instruction editing. The model is equipped with the AudioCoT dataset, which contains audio data annotated with chains of thought. On the VGGSound dataset, ThinkSound surpasses 6 mainstream methods (Seeing&Hearing, V-AURA, FoleyCrafter, Frieren, V2A-Mapper, and MMAudio), demonstrating outstanding performance.
Main Features of ThinkSound
- Basic Sound Effect Generation: Generates basic sound effects that semantically and temporally match the video content, providing an initial audio background for the video.
- Interactive Object-Level Refinement: Supports users clicking on specific objects in the video to refine and optimize the sound effects of specific objects, making the sound effects fit specific visual elements more precisely.
- Instruction-Driven Audio Editing: Supports users editing the generated audio based on natural language instructions, such as adding, deleting, or modifying specific sound effects, to meet different creative needs.
Technical Principles of ThinkSound
- Chain-of-Thought Reasoning: Decomposes the audio generation task into multiple reasoning steps, including analyzing visual dynamics, inferring acoustic attributes, and synthesizing sound effects in chronological order, imitating the creative process of a human sound designer.
- Multimodal Large Language Model (MLLM): Based on models such as VideoLLaMA2, it extracts the spatiotemporal information and semantic content of videos and generates structured CoT reasoning chains, providing detailed guidance for audio generation.
- Unified Audio Foundation Model: Based on conditional flow matching technology, it combines video, text, and audio context information to generate high-fidelity audio. The model supports any combination of input modalities and flexibly handles different generation and editing tasks.
- Dataset Support: Based on the AudioCoT dataset, it provides audio data with structured CoT annotations, used for training and optimizing the model to improve understanding and generation of the relationship between sound and visuals.
Project Address of ThinkSound
- Project Website：https://thinksound-project.github.io/
- GitHub Repository：https://github.com/liuhuadai/ThinkSound
- HuggingFace Model Library：https://huggingface.co/liuhuadai/ThinkSound
- arXiv Technical Paper：https://arxiv.org/pdf/2506.21448
Application Scenarios of ThinkSound
- Film and Television Production: Generates realistic background sound effects and sound effects for specific scenes for movies, TV series, and short videos, enhancing the audience's sense of immersion and strengthening the realism of synchronized picture and sound.
- Game Development: Generates dynamic environmental sound effects and interactive sound effects for game scenes, enhancing players' immersion and interactivity and improving the gaming experience.
- Advertising and Marketing: Generates attractive sound effects and background music for advertising videos and social media content, enhancing the appeal and reach of the content and boosting brand influence.
- Education and Training: Generates sound effects matching the content for online education videos and simulated training environments, helping students better understand and remember, and improving learning outcomes and training quality.
- Virtual Reality (VR) and Augmented Reality (AR): Generate sound effects in VR and AR applications that highly match the virtual environment, enhancing the user's sense of immersion and interactivity, and providing a more personalized experience.