Share your thoughts, 1 month free Claude Pro on usSee more
WorkDL logo mark

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 Lahrichi, Hakim Missoum, Joan Serr\`a, Yuki Mitsufuji• 2026

Related benchmarks

TaskDatasetResultRank
Text-to-Audio GenerationAudioCaps (test)
KL Divergence1.599
195
Text-to-Audio RetrievalAudioCaps (test)--
180
Audio-to-Text RetrievalAudioCaps (test)--
69
Sound effects generationSound Effects (test)
FAD0.58
22
Video-to-Audio GenerationVGG-Sound
Fréchet Distance (FD)19.442
10
Sound effects generationBBC Sound Effects Dataset
FAD0.58
8
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
Showing 10 of 13 rows

Other info

Follow for update