ThinkSound: Chain-of-thought reasoning for video-to-audio generation 🎵
A video-to-audio generation model that thinks through what sounds should happen in your videos. Unlike traditional models that just match sounds to objects, ThinkSound reasons about what sounds should happen and when, creating natural audio that fits the mood, timing, and story of your video.
Note:
The code, models, and dataset are for research and educational purposes only.
Commercial use is NOT permitted.For commercial licensing, please contact the authors.
What ThinkSound does ✨
ThinkSound transforms silent videos into rich audio experiences by: - Understanding visual content: Analyzes what’s happening in your video - Reasoning about audio: Thinks through what sounds should occur and why - Creating contextual audio: Generates audio that matches the mood and timing - Following your guidance: Uses your descriptions to create exactly what you want - Producing professional quality: Outputs 44.1kHz audio suitable for any use
Model capabilities 🎧
ThinkSound uses step-by-step reasoning to understand not just what’s in your video, but why certain sounds should happen and how they should relate to each other. It generates audio that naturally fits the visual content, timing, and emotional context.
Key features:
🧠 Step-by-step reasoning for intelligent audio design decisions
🎬 Video-aware generation that matches visual events and timing
📝 Text conditioning with captions and detailed descriptions
🎛️ Professional controls for quality and creativity adjustment
⏱️ Flexible duration support for videos of various lengths
🎵 High-quality output at 44.1kHz for professional use
How to get the best results 🌟
Basic approach: - Upload your video file - Add a brief caption describing what’s happening - ThinkSound will create appropriate audio automatically
Advanced control: - Use the detailed description field to describe exactly what audio you want - Be specific about layers, timing, and emotional tone - Adjust settings to balance quality and creativity
Example descriptions:
For a cooking video:
- Caption: “Cooking pasta in a kitchen”
- Detailed description: “Begin with sizzling sounds from the pan, add gentle chopping noises, include ambient kitchen sounds like cabinet doors and running water. Layer in the bubbling of boiling water.”
For a nature scene:
- Caption: “Forest wildlife”
- Detailed description: “Create layered nature sounds with bird calls in the foreground, rustling leaves in mid-ground, and distant wind through trees. Add occasional animal sounds like squirrel chatter.”
For a rain scene:
- Caption: “Rain on window”
- Detailed description: “Start with gentle raindrops hitting glass, gradually building to steady rainfall. Add subtle ambient sounds of water flowing and distant thunder rumbling.”
Parameter controls 🎛️
cfg_scale (1.0-20.0): Controls how closely the model follows your text descriptions. Higher values stick closer to your prompts, lower values allow more creative interpretation.
num_inference_steps (10-100): Quality vs speed balance. More steps generally mean higher quality but take longer to generate.
seed: Set a specific number for reproducible results, or leave empty for random variations each time.
What makes ThinkSound special 🚀
Traditional video-to-audio models often produce generic or mismatched sounds. ThinkSound changes this by:
- Reasoning through audio design: Doesn’t just recognize objects and play sounds—thinks about how different elements interact and what the complete audio landscape should be
- Understanding context: Grasps not just what’s in the video, but the mood, setting, and narrative context
- Temporal awareness: Creates audio that follows the natural flow and timing of events in the video
- Multi-modal conditioning: Combines visual information with your text descriptions for precise control
- Professional quality: Generates audio suitable for production use
Best use cases 🎯
ThinkSound excels at:
- Film and video post-production: Adding professional soundtracks to footage
- Content creation: Enhancing social media videos and presentations
- Silent film restoration: Bringing historical footage to life with period-appropriate audio
- Educational content: Creating engaging audio for instructional videos
- Game development: Generating contextual audio for cutscenes and trailers
- Accessibility: Adding audio descriptions and soundscapes for visual content
Limitations to consider ⚠️
- Works best with clear, well-lit video content
- Very abstract or unusual content may produce unexpected results
- Complex multi-layered audio scenes work better with detailed descriptions
- Processing time varies with video length and quality settings
- Creative results depend on the specificity of your text descriptions
Research background 📚
ThinkSound is based on research in multimodal machine learning and step-by-step reasoning. The model was developed by the FunAudioLLM team and represents an advance in video-to-audio generation.
Original research: ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing
Important licensing note 📝
This model is for research and educational purposes only.
Commercial use is NOT permitted without explicit licensing from the original authors.
For commercial licensing, please contact the original research team.