Sony AI has released Woosh, a foundation model built specifically for generating sound effects, filling a gap that most generative audio systems have overlooked in favor of music or general audio creation. The model supports both text-to-audio generation, where users describe a sound in words, and video-to-audio generation, where the system creates sound directly from video footage with optional text guidance.

Why Sound Effects Deserve Their Own AI Model?

The insight behind Woosh reveals something important about generative AI: one-size-fits-all models often underperform when applied to specialized tasks. Professional sound design requires fundamentally different training data and controls than general audio systems. The research team discovered a significant gap between public and private training data, with models trained on licensed professional sound effect libraries substantially outperforming those trained on publicly available datasets.

Mark Ferras and Hakim Missoum, two of the researchers behind Woosh, explained the motivation. "The impetus was to create a model that was tailored to sound effects, but also meets the requirements and expectations of professional audio creators and sound designers, rather than just having a model online for amateur content creators," Missoum noted. The team had access to Sony's professional audio experts, which shaped the model's design from the ground up.

"The big thing that you hear not only in the audio space but generally from artists, when it comes to generative AI, is the need for more controllability with generative output, and so that's something we really took to heart and tried to implement in our models," said Hakim Missoum.

Hakim Missoum, Researcher at Sony AI

How Does Woosh Compare to Other Audio Generators?

Sony released both a public and private version of Woosh. The private model, trained on commercial libraries including Pro Sound Effects and BOOM, delivers studio-grade output optimized for professional workflows. The public model uses the same architecture but trains on publicly available datasets, making it accessible to researchers and developers.

When benchmarked against comparable open-source models like StableAudio-Open and TangoFlux, Woosh showed competitive or better performance across multiple components. The public release includes several specialized modules designed for different tasks:

• Audio Encoder/Decoder (Woosh-AE): Provides high-quality latent encoding and decoding, allowing the system to work with audio at a compressed level for faster processing.
• Text Conditioning (Woosh-CLAP): A multimodal model that aligns text descriptions with audio, enabling precise control over what sounds the system generates.
• Text-to-Audio Generation (Woosh-Flow and Woosh-DFlow): Original and distilled versions that generate sound effects from written descriptions, with the distilled version optimized for fast inference on lower-resource systems.
• Video-to-Audio Generation (Woosh-VFlow and Woosh-DVFlow): Multimodal models that create audio directly from video sequences, with optional text prompts to guide the output.

The distilled models are particularly noteworthy because they allow for low-resource operation and fast inference, meaning sound designers can generate effects quickly without needing expensive computing hardware.

What Problem Does This Solve for Creators?

Sound designers typically work through an iterative process, testing different audio options to find the right fit for a project. Woosh aims to accelerate this workflow by giving creators access to a broader palette of sounds. "The main goal is to help creators such as sound designers work faster," explained Mark Ferras. Rather than manually searching libraries or recording new sounds, designers can generate variations instantly and refine them based on their needs.

The model was built with gaming, film, and interactive media in mind, suggesting its primary use cases involve projects where sound design is critical but time-consuming. The video-to-audio capability is particularly relevant for filmmakers and game developers who need to match audio to visual content.

How to Use Woosh for Sound Design Projects?

Sony has made Woosh available to the research community with open-source code and model weights for non-commercial use. Developers and researchers can access the full pipeline, including the encoder/decoder, text-conditioning, and diffusion models:

• Access the Code: Inference code and model weights are available on GitHub at https://github.com/SonyResearch/Woosh for researchers and non-commercial projects.
• Explore Demos: Demo samples and interactive exploration tools are available at https://sonyresearch.github.io/Woosh/ to hear what the model can generate.
• License the Private Model: For professional studios and commercial use, Woosh-Flow Private is available for licensing, trained on studio-quality commercial sound libraries at https://sonyresearch.github.io/Woosh/flow-private.html.

The training process reveals the scale of effort behind professional-grade audio AI. The private model was trained on approximately one million samples totaling 5,500 hours of commercial audio, including commercially-licensed sound effect libraries and music stems. The public model, while trained on smaller datasets, still demonstrates competitive performance on public benchmarks.

What Does This Mean for the Broader AI Audio Landscape?

Woosh's release highlights a broader trend in generative AI: specialized models often outperform general-purpose systems on specific tasks. While music generation has received significant attention from companies like Suno and Google, sound effects generation remained largely overlooked despite its importance to creative industries. Sony's research suggests that as generative AI matures, we may see more domain-specific models designed for particular professional workflows rather than one-size-fits-all solutions.

The gap between public and private models also underscores a challenge in open-source AI development. While Sony released public weights to support the research community, the highest-quality version remains proprietary and available only through licensing. This two-tier approach allows researchers to experiment while protecting Sony's investment in commercial-grade tools.

As generative audio tools become more sophisticated and accessible, the music industry faces separate challenges. A recent empirical analysis found that 93% of AI-generated music on Spotify receives few or no listener plays, with most AI musicians releasing large volumes of music across multiple genres in hopes of generating hits. This suggests that while specialized tools like Woosh may help professional creators work more efficiently, the broader ecosystem still grapples with quality control and the proliferation of low-effort AI content.