Alibaba's ThinkSound Goes Open Source: AI Dubbing Now Understands a Video's Subtext with 'Chain of Thought'

Imagine an AI that not only adds sound to a video but also understands every dynamic detail, from a bird’s flapping wings to rustling leaves, and allows you to modify the sound effects in real-time like a director. Alibaba’s open-source ThinkSound model, through its innovative ‘Chain of Thought’ technology, is making this a reality, completely changing our perception of AI audio generation.

Have you ever had this experience? You’re watching an AI-generated video with stunning visuals, but the sound just feels… off. A bird’s call sounds abruptly, out of sync with its flight; a car passing by lacks the layered sound of approaching and receding. This audio-visual desynchronization is the Achilles’ heel of traditional AI dubbing technology.

Previous Video-to-Audio models were like interns who could only “describe what they see.” You give them a video, they identify an “owl” in the frame, and then they pair it with a monotonous “owl hoot.” As for when the owl flaps its wings, when it flies off the branch, and what environmental sounds its wing beats create—sorry, it can’t comprehend any of these details.

But now, things have taken a revolutionary turn. Alibaba’s voice AI team has open-sourced the world’s first audio generation model that supports “chain-of-thought” reasoning—ThinkSound. It no longer just matches sounds to images; it has truly learned to “think,” capable of structurally understanding a video’s dynamic narrative to generate highly synchronized, detail-rich spatial audio.

The Bottleneck of Traditional AI Dubbing: It Can “See,” But It Can’t “Hear”

Let’s first look at where the problem lies. Traditional end-to-end models, when processing videos, often ignore the temporal and spatial correlation between sound and visual events. Their operation is rather simplistic: identify object → generate corresponding sound.

Using the owl in the image above as an example, a traditional model might receive the command “an owl is hooting.” Consequently, the audio it outputs is just a single bird call. It completely fails to capture the series of consecutive actions in the video, such as “perching and hooting (t1),” “preparing to flap wings (t2),” and “flying off the branch, causing leaves to rustle (t3).” The result is audio that lacks a sense of time and complex detail, sounding both unrealistic and lifeless.

The ThinkSound Revolution: Introducing “Chain of Thought” to Teach AI to Think

ThinkSound’s biggest breakthrough is the introduction of “Chain-of-Thought” (CoT) technology into the audio generation field for the first time. So, what is a chain of thought?

Simply put, it’s like installing an “inner monologue” mechanism for the AI. Before generating audio, ThinkSound first engages in a series of thoughts and plans about the video.

Using the same owl example, ThinkSound’s process is as follows:

1. Structured Analysis: The model first forms a reasoning chain in its “mind”: “First, the owl hoots on the branch. Then, it flaps its wings to prepare for takeoff. Finally, it flies off the branch, and this action simultaneously causes the leaves to rustle.”
2. Audio Generation: Based on this structured “script,” ThinkSound sequentially generates the corresponding audio: bird hoot → wing flapping sound → leaf rustling sound.

Do you see the difference? Through the chain of thought, the AI is no longer randomly piecing sounds together but can understand the sequence and causal relationships of events, thereby creating a soundscape that is perfectly synchronized with the visuals and rich in layers.

Not Just Generation, But Interactive Editing: Your Video, Your Command

ThinkSound’s power doesn’t stop there. It not only generates but also allows users to perform “interactive” step-by-step editing, making you the director of the sound effects.

This process is also phased and highly flexible:

• Phase 1: The AI has already generated the basic audio containing the hoot, wing flaps, and leaf sounds based on its chain of thought.
• Phase 2 (Optional): If you feel the sound of the leaves is too prominent, you can give a command in natural language: “Extract the sound made by the owl and avoid including the sound of the trees.” The model will use visual grounding techniques (like Grounded-SAM-2) to precisely isolate the owl’s sound, generating a purer audio track.
• Phase 3 (Optional): If you want to enrich the scene further, you can continue with another command: “Add the calls of other birds, but retain the characteristics of the original owl’s hoot.” The model will then overlay new background sounds without affecting the main sound effect.

This interactive generation and editing capability gives creators unprecedented control, allowing for more precise realization of their auditory imagination, whether for film post-production or personal content creation.

The “Mental Food” for AI: The High-Quality AudioCoT Dataset

Behind every intelligent model, there is a vast amount of high-quality “teaching material.” To train ThinkSound’s structured reasoning ability, the Alibaba team built a multimodal dataset called AudioCoT.

This dataset is massive, containing 2531.8 hours of high-quality audio samples, integrating real-world sounds from various sources like VGGSound and AudioSet, covering everything from animal calls to mechanical operations. More importantly, the dataset is meticulously designed with a large number of “object-level” and “instruction-level” samples, specifically for training the model to handle complex commands like “extract sound A while avoiding interference from sound B.”

The Power of Open Source: The Future and Potential of ThinkSound

Experimental data shows that ThinkSound’s performance is outstanding. On the VGGSound test set, its core metrics improved by over 15% compared to mainstream methods, and it also significantly outperformed Meta’s similar model in the MovieGen Audio Bench test.

Even more exciting is that the Alibaba voice AI team has fully open-sourced ThinkSound’s code and pre-trained weights, which are available for free on GitHub, HuggingFace, and the ModelScope community. This will undoubtedly greatly accelerate the popularization of technology in the audio generation field.

• GitHub Address: https://github.com/FunAudioLLM/ThinkSound
• HuggingFace Address: https://huggingface.co/spaces/FunAudioLLM/ThinkSound

It is important to note that the current open-source version is for research and educational purposes only. For commercial use, you need to contact the authors for authorization.

Looking ahead, the potential of ThinkSound is limitless. It can not only provide powerful new tools for film and television sound production and audio post-processing but could also be applied to game development, virtual reality (VR), and other scenarios requiring immersive experiences, redefining the boundaries of sound in human-computer interaction.

The open-sourcing of this technology signifies that AI is evolving from a mere execution tool into a creative partner that can understand context and collaborate with humans. For all content creators, a more intelligent and efficient era of sound creation has arrived.