thinksound-audio-generation-reasoning

name	thinksound-audio-generation-reasoning
title	ThinkSound: Chain-of-Thought Reasoning in Multimodal LLMs for Audio Generation and Editing
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2506.21448
keywords	["AudioGeneration","AudioEditing","Multimodal","ChainOfThought","DiffusionTransformer"]
description	Generates high-quality audio through three-stage CoT reasoning pipeline: foundational foley synthesis, object-focused refinement, and instruction-guided editing. Uses fine-tuned VideoLLaMA for reasoning and flow-matching audio foundation model. Apply for professional audio design workflows or video-to-audio applications.

ThinkSound: Professional Audio Design Through Reasoning-Guided Synthesis

Professional audio designers don't generate sound directly—they reason about what's needed, build foundational elements, refine specific sounds, then polish through iterative editing. Most audio generation systems treat this as a single black-box transformation, failing to capture the reasoning that distinguishes professional results from naive synthesis. ThinkSound introduces a three-stage pipeline guided by chain-of-thought reasoning from a fine-tuned multimodal LLM, decomposing sound design into deliberate stages. The result is higher-quality audio with better temporal coherence and semantic accuracy than end-to-end systems.

The insight is that sound design involves compositional reasoning—understanding which sounds matter, in what order, with what properties. Explicit decomposition through reasoning captures this structure.

Core Concept

ThinkSound replaces single-pass audio generation with a three-stage reasoning-guided process:

1. Stage 1: Foundational Foley Analysis: Fine-tuned VideoLLaMA generates comprehensive chain-of-thought analyzing the entire video, identifying all sound events, acoustic properties, and temporal dependencies.

2. Stage 2: Object-Focused Refinement: User can click on video regions (ROI selection) to focus refinement on specific objects, with LLM reasoning about their sounds.

3. Stage 3: Instruction-Guided Editing: Natural language editing instructions are decomposed through CoT into specific audio modifications (pitch, gain, reverb).

The audio foundation model is a multimodal diffusion transformer using flow matching, accepting combinations of video, text, and audio inputs. This flexibility enables the three-stage pipeline.

Architecture Overview

• Reasoning Component: Fine-tuned VideoLLaMA2 generating structured chain-of-thought about sound design
• Audio Foundation Model: Conditional flow-matching transformer synthesizing from multimodal inputs
• Dual-Pathway Text Encoding: MetaCLIP for scene context, T5 for detailed reasoning
• Adaptive Fusion: Combines video and audio features flexibly
• Three-Stage Pipeline: Foundational → refinement → editing with reasoning at each stage
• AudioCoT Dataset: Paired reasoning annotations and audio for supervision

Implementation

Reasoning-guided audio generation with VideoLLaMA:

import torch
import torch.nn  nn
 typing  , , 

 (nn.Module):
    
     ():
        ().__init__()
        .reasoning_model = reasoning_model  
        .audio_model = audio_model  
        .device = device

     () -> [, torch.Tensor]:
        
        
        num_frames = video.shape[]
        key_frame_indices = [, num_frames // , num_frames // , *num_frames // , num_frames - ]
        key_frames = video[key_frame_indices]

        
        reasoning_prompt = 

         torch.no_grad():
            reasoning_output = .reasoning_model.generate(
                video=key_frames,
                prompt=reasoning_prompt,
                max_length=
            )

        reasoning_trace = reasoning_output[]

        
        sound_events = ._parse_sound_events(reasoning_trace)

        
        audio = .audio_model.synthesize(
            video=video,
            text_guidance=reasoning_trace,  
            sound_events=sound_events,
            num_samples= * (num_frames // fps)  
        )

         reasoning_trace, audio

     () -> [, torch.Tensor]:
        
        
        roi_frames = video * roi_mask.unsqueeze(-)

        
         roi_description:
            refine_prompt = 
        :
            refine_prompt = 

         torch.no_grad():
            reasoning_output = .reasoning_model.generate(
                video=roi_frames,
                prompt=refine_prompt,
                max_length=
            )

        refinement_reasoning = reasoning_output[]

        
        roi_audio = .audio_model.synthesize(
            video=roi_frames,
            text_guidance=refinement_reasoning,
            mask=roi_mask,
            condition_on_audio=audio,  
            num_samples=audio.shape[]
        )

        
        refined_audio = ._blend_roi_audio(audio, roi_audio, roi_mask)

         refinement_reasoning, refined_audio

     () -> [, torch.Tensor]:
        
        
        editing_prompt = 

         torch.no_grad():
            reasoning_output = .reasoning_model.generate(
                text=editing_prompt,
                video=video  video     ,
                max_length=
            )

        editing_reasoning = reasoning_output[]

        
        editing_params = ._parse_editing_params(editing_reasoning, instruction)

        
        edited_audio = .audio_model.edit_audio(
            audio=audio,
            text_guidance=instruction,
            editing_params=editing_params,
            reasoning=editing_reasoning
        )

         editing_reasoning, edited_audio

     () -> :
        
        
        
        events = []
        
         re
        pattern = 
        matches = re.findall(pattern, reasoning_text)
         time_str, description  matches:
            events.append({
                : (time_str),
                : description.strip()
            })
         events

     () -> torch.Tensor:
        
        
        
        blend_factor =   

        
        blended = full_audio * ( - blend_factor) + roi_audio * blend_factor

         blended

     () -> :
        
        params = {
            : ,
            : ,
            : 
        }

        
         re

           instruction.lower():
            params[] =   
           instruction.lower():
            params[] = -

           instruction.lower():
            params[] = 

        
        db_match = re.search(, reasoning_text)
         db_match:
            params[] = (db_match.group())

         params