WavFlow: Audio Generation in Waveform Space
Summary
WavFlow generates high-fidelity audio directly in raw waveform space using waveform patchify and amplitude lifting, achieving competitive performance on video-to-audio and text-to-audio benchmarks without intermediate latent representations.
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Paper page - WavFlow: Audio Generation in Waveform Space
Source: https://huggingface.co/papers/2605.18749
Abstract
WavFlow generates high-fidelity audio directly in raw waveform space using waveform patchify and amplitude lifting, achieving competitive performance on video-to-audio and text-to-audio benchmarks without intermediate latent representations.
Modern audio generation predominantly relies onlatent-space compression, introducing additional complexity and potential information loss. In this work, we challenge this paradigm with WavFlow, a framework that generates high-fidelity audio directly inraw waveform spacewithout intermediate representations. To overcome the inherent difficulties of modeling high-dimensional and low-energy signals, we reshape audio into 2D token grids throughwaveform patchifyand introduceamplitude liftingto align signal scales, enabling stable optimization via directx-predictioninflow matching. To capture complex semantic alignment and temporal synchronization, we leverage an automated data pipeline to curate 5 million high-qualityvideo-text-audio triplets, allowing the model to learn fine-grained acoustic patterns from scratch. Experimental results show that WavFlow achieves competitive performance on the video-to-audio benchmarkVGGSound(FD_PaSST: 59.98, IS_PANNs: 17.40, DeSync: 0.44) and the text-to-audio benchmarkAudioCaps(FD_PANNs: 10.63, IS_PANNs: 12.62), matching or exceeding the performance of established latent-based methods. Our work demonstrates that intermediate compression is not a prerequisite for high-quality synthesis, offering a simpler and more scalable alternative formultimodal audio generation.
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