Woosh: A Sound Effects Foundation Model
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915966874550272 |
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| author | Hadjeres, Gaëtan Ferras, Marc Koutini, Khaled Weck, Benno Bittar, Alexandre Hummel, Thomas Lahrichi, Zineb Missoum, Hakim Serrà, Joan Mitsufuji, Yuki |
| author_facet | Hadjeres, Gaëtan Ferras, Marc Koutini, Khaled Weck, Benno Bittar, Alexandre Hummel, Thomas Lahrichi, Zineb Missoum, Hakim Serrà, Joan Mitsufuji, Yuki |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01929 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Woosh: A Sound Effects Foundation Model Hadjeres, Gaëtan Ferras, Marc Koutini, Khaled Weck, Benno Bittar, Alexandre Hummel, Thomas Lahrichi, Zineb Missoum, Hakim Serrà, Joan Mitsufuji, Yuki Sound Artificial Intelligence Machine Learning 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/. |
| title | Woosh: A Sound Effects Foundation Model |
| topic | Sound Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.01929 |