WavFlow: Flowing Through Waveforms for Audio Generation

Modern audio generation predominantly relies on latent-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 in raw waveform space without intermediate representations. To overcome the inherent difficulties of modeling high-dimensional and low-energy signals, we reshape audio into 2D token grids through waveform patchify and introduce amplitude lifting to align signal scales, enabling stable optimization via direct x-prediction in flow matching. To capture complex semantic alignment and temporal synchronization, we leverage an automated data pipeline to curate 5M high-quality video-text-audio triplets, allowing the model to learn fine-grained acoustic patterns from scratch. Experimental results show that WavFlow achieves state-of-the-art results on the video-to-audio benchmark VGGSound (FDPaSST 55.82, ISPANNs 17.40, DeSync 0.44) and the text-to-audio benchmark AudioCaps (FD_PANNs 10.63), outperforming established latent-based methods. Our work demonstrates that such intermediate compression is not a prerequisite for high-quality synthesis, offering a simpler and more scalable alternative for multimodal audio generation.