This is a simplified guide to an AI model called Thinksound maintained by Zsxkib. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
Model overview
thinksound takes a unique approach to video-to-audio generation by incorporating step-by-step reasoning rather than simple pattern matching. Unlike models that just match sounds to objects, this framework thinks through what sounds should happen and when, creating natural audio that fits the mood, timing, and context of your video. While mmaudio focuses on high-quality audio synthesis from video content, thinksound adds a layer of contextual reasoning that considers visual dynamics, acoustic environments, and temporal relationships. The model was developed by zsxkib and uses Chain-of-Thought (CoT) reasoning to guide a unified audio foundation model through three complementary stages: foundational foley generation, interactive object-centric refinement, and targeted editing guided by natural language instructions.
Model inputs and outputs
The model processes video files and generates contextually appropriate audio tracks through an intelligent reasoning process. Users can provide optional text descriptions and detailed step-by-step reasoning instructions to guide the audio generation process.
Inputs
- video: Input video file in various formats (MP4, AVI, MOV, etc.)
- caption: Brief description of video content (optional but recommended)
- cot: Chain-of-Thought description providing detailed reasoning about desired audio (optional)
- cfg_scale: Classifier-free guidance scale (1.0-20.0, default 5.0) controlling how closely the model follows text descriptions
- num_inference_steps: Number of diffusion denoising steps (10-100, default 24) for quality vs speed tradeoff
- seed: Random seed for reproducible outputs (optional)
Outputs
- Audio file: Generated audio track that matches the video content with contextual reasoning applied
Capabilities
The model processes videos at dual fra...
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