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Woosh: A Sound Effects Foundation Model

About

The audio research community depends on open generative models as foundational tools for building novel approaches and establishing baselines. In this report, we present Woosh, Sony AI's publicly released sound effect foundation model, detailing its architecture, training process, and an evaluation against other popular open models. Being optimized for sound effects, we provide (1) a high-quality audio encoder/decoder model and (2) a text-audio alignment model for conditioning, together with (3) text-to-audio and (4) video-to-audio generative models. Distilled text-to-audio and video-to-audio models are also included in the release, allowing for low-resource operation and fast inference. Our evaluation on both public and private data shows competitive or better performance for each module when compared to existing open alternatives like StableAudio-Open and TangoFlux. Inference code and model weights are available at https://github.com/SonyResearch/Woosh. Demo samples can be found at https://sonyresearch.github.io/Woosh/.

Ga\"etan Hadjeres, Marc Ferras, Khaled Koutini, Benno Weck, Alexandre Bittar, Thomas Hummel, Zineb Lahrici, Hakim Missoum, Joan Serr\`a, Yuki Mitsufuji• 2026

Related benchmarks

TaskDatasetResultRank
Text-to-Audio GenerationAudioCaps (test)--
154
Text-to-Audio RetrievalAudioCaps (test)--
152
Audio-to-Text RetrievalAudioCaps (test)--
69
Video-to-Audio GenerationVGG-Sound
Fréchet Distance (FD)19.442
10
Video-to-Audio GenerationFoleyBench (test)
FD24.136
7
Video-to-Audio GenerationOGameData (test)
FD10.859
7
Text-to-Audio GenerationInternalSFX (test)
FD246.9
6
Audio ReconstructionAudioCaps public (test)
Mel Distance0.032
5
Audio ReconstructionInternalSFX (test)
MelDist0.021
5
Audio-to-Text RetrievalInternalSFX (test)
R@569.4
3
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