Woosh

Woosh - Sound Effect Generative Models

This repository provides inference code and open weights for the sound effect generative models developed at Sony AI. The
current public release includes four models addressing the text-to-audio (T2A) and video-to-
audio (V2A) tasks:

- Audio encoder/decoder (Woosh-AE): High-quality latent encoder/decoder providing latents
for generative modeling and decoding audio from generated latents.

- Text conditioning (Woosh-CLAP): Multimodal text-audio alignment model providing token la-
tents for diffusion model conditioning.

- T2A Generation (Woosh-Flow and Woosh-DFlow): Original and distilled LDMs generating au-
dio unconditionally or from given a text prompt.

- V2A Generation (Woosh-VFlow): Multimodal LDM generating audio from a video sequence
with optional text prompts.

Installation

Start by installing uv first

bash
 pip install uv

and then the Woosh environment, with either:

cpu support,

bash
uv sync --extra cpu

or cuda support,

bash
uv sync --extra cuda

Download model weights

Open model weights are available for all Woosh models trained on public datasets. You can download
and unzip the pretrained weights from the releases
page, or otherwise using the github CLI as

bash
gh release download v1.0.0
unzip '*.zip'

The checkpoints should be located in folders named checkpoints/MODEL_NAME, each containing config and weight files.

Download media samples

We provide audio samples to be used as inputs to our test_Woosh-*.py test scripts. You can download
and unzip the file samples.zip from the releases
page, or otherwise using the github CLI as

bash
gh release download v1.0.0 -p 'samples.zip'
unzip samples.zip

Usage

Test scripts
An inference test script for every model is provided. Just run any of the following

python
uv run test_Woosh-AE.py
uv run test_Woosh-Flow.py
uv run test_Woosh-DFlow.py
uv run test_Woosh-VFlow.py
uv run test_Woosh-DVFlow.py
uv run test_Woosh-CLAP.py

and the generated audio/video will be written to outputs/ as .wav/.mp4 audio/video files.

Check our tech report on arxiv.org for a description of all models.

Gradio demos
Two basic Gradio demos, for Woosh-Flow and Woosh-DFlow models, are available. To launch a Gradio demo locally, run one of the following

python
uv run gradio_Woosh-Flow.py
uv run gradio_Woosh-DFlow.py

Open a web browser on the same machine and access the demo at https://127.0.0.1:7860.

API server
Woosh models can be served via our API server. Check the API folder for usage details.

Citation
For details about model architecture, training and evaluation, please check our tech report
available on arxiv.org.

bibtex
@misc{hadjeres2026,
      title={Woosh: A Sound Effects Foundation Model},
      author={Gaetan Hadjeres, Marc Ferras, Khaled Koutini, Benno Weck, Alexandre Bittar, Thomas Hummel, Zineb Lahrichi, Hakim Missoum, Joan Serrà and Yuki Mitsufuji},
      year={2026},
      eprint={2604.01929},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2604.01929},
}

Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.

Please make sure to updat

……（后略，见原文）