Abstract
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 this https URL. Demo samples can be found at this https URL.
Abstract (translated)
音频研究社区依赖开源生成模型作为基础工具，以构建新颖方法并建立基准。在本报告中，我们介绍索尼AI公开发布的音效基础模型Woosh，详细说明其架构、训练过程，并与其他流行开源模型进行对比评估。针对音效优化，我们提供了（1）高质量的音频编码器/解码器模型和（2）用于条件控制的文本-音频对齐模型，以及（3）文本到音频和（4）视频到音频的生成模型。本次发布还包含蒸馏后的文本到音频与视频到音频模型，支持低资源运行与快速推理。我们在公开及私有数据上的评估显示，与StableAudio-Open和TangoFlux等现有开源方案相比，各模块均具有竞争力或更优表现。推理代码与模型权重可通过此https链接获取，演示样本可见于此https链接。
URL
https://arxiv.org/abs/2604.01929
