AB
AB
AiBoss

All Categories

project

AudioX - A diffusion converter model jointly developed by HKUST and Dark Side of the Moon, enabling audio generation from arbitrary content.

AudioX is a unified diffusion transformer model jointly proposed by the Hong Kong University of Science and Technology and Dark Side of the Moon, specifically designed for generating audio and music from arbitrary content. The model can handle various input modalities, including text, video, images, music, and...

What is AudioX?

AudioX is a unified diffusion transformer model jointly proposed by the Hong Kong University of Science and Technology and Dark Side of the Moon, specifically designed for generating audio and music from arbitrary content. The model can handle multiple input modalities, including text, video, images, music, and audio, generating high-quality audio output. Its core innovation lies in a multimodal masking training strategy, which forces the model to learn from incomplete inputs by randomly masking the input modalities, thereby enhancing its cross-modal representation capabilities.

AudioX's main functions

  • Multimodal input support
    • Text-to-AudioIt generates corresponding sound effects based on text descriptions. For example, given the input "dog barking," the model can generate audio of a dog barking.
    • Video to AudioThis feature generates sound effects that match the video content. For example, given a video of a car driving, the model can generate the sound of the car engine.
    • Image-to-AudioThis function generates corresponding sound effects based on image content. For example, given an image of a storm, the model can generate the sound of a storm.
    • Music GenerationThis feature generates music based on text descriptions or video content. For example, given the input "relaxing piano music," the model can generate a piece of relaxing piano music.
    • Audio InpaintingThis function repairs missing parts of audio based on contextual information. For example, given an audio clip with blank spaces, the model can fill in the blanks to make the audio complete.
    • Music CompletionThis function generates subsequent parts of a given musical passage. For example, given the beginning of a piece of music, the model can generate the rest of the music.
  • High-quality audio generationAudioX uses diffusion model technology to generate high-quality, high-fidelity audio and music, ensuring that the generated audio closely resembles real audio in terms of sound quality and detail.
  • Flexible natural language controlUsers can precisely control the generated audio content through natural language descriptions. For example, users can specify the type of sound effects, the style of music, the instruments used, etc., to make the generated audio more in line with their needs.
  • Cross-modal learning capabilityAudioX can handle inputs from multiple modalities, effectively integrating them to generate audio that matches the input conditions. For example, when given both text and video inputs, the model can comprehensively consider the semantic information of both to generate audio that is more relevant to the scene.
  • Strong generalization abilityIt performs well on multiple datasets and tasks, including AudioCaps, VGGSound, MusicCaps, and V2M-bench, demonstrating its generalization ability and adaptability in different scenarios.
  • Zero-sample generation capabilityWithout specific training for a particular modality (such as images), AudioX can generate high-quality audio under zero-sample conditions, demonstrating powerful general-purpose generation capabilities.

AudioX technical principles

  • Diffusion ModelAudioX uses the core idea of a diffusion model to gradually add noise to the input data, and then gradually remove the noise through a reverse process, ultimately generating high-quality audio or music.
    • Forward diffusion processGaussian noise is gradually added to the input data to generate a series of noisy latent variables.
    • Inverse denoising processBy training a denoising network (usually a Transformer), noise is gradually removed to reconstruct clean audio data.
  • Multimodal mask training strategyTo enhance the model's cross-modal learning capability, AudioX employs a multimodal masking training strategy. During training, the model randomly masks parts of the input modalities, forcing it to learn from incomplete inputs and improving its robustness and generalization ability.
  • Multimodal encoders and decodersAudioX integrates multiple dedicated encoders to process input data from different modalities, and then fuses these encoded features into a unified latent space.
    • Video encoder: Use CLIP-ViT-B/32 to extract features from video frames.
    • Text encoderUse T5-base to extract text features.
    • Audio encoderUse an autoencoder to extract features from the audio.
    • Feature fusionFeatures from different modalities are fused into a unified multimodal embedding vector through linear transformation and concatenation operations.
  • Conditional embedding in the diffusion processDuring the diffusion process, multimodal embedding vectors serve as conditional inputs, helping the model generate audio or music consistent with the input conditions. The fused multimodal features are input into the diffusion model along with the diffusion time steps. By progressively removing noise, high-quality audio or music matching the input conditions is generated.
  • Dataset and TrainingTo train AudioX, researchers constructed two large-scale multimodal datasets: vggsound-caps, based on the VGGSound dataset, containing 190K audio captions; and V2M-caps, based on the V2M dataset, containing 6 million music captions.

AudioX project address

Applications of AudioX

  • Video background musicIt can automatically generate background music or sound effects based on the video content, enhancing the video's appeal and emotional resonance.
  • Animation sound effectsGenerate matching sound effects for animated scenes, such as footsteps, wind sounds, and explosions, to enhance the immersive experience of the animation.
  • Music generationIt generates music based on text descriptions or style requirements, providing inspiration or assistance to music creators.
  • Language learningGenerate sound effects or background music related to language learning content to enhance the learning experience.
Back