Audioseal
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Facebook AI Research
- Organisation type
- Industry
- Country
- United States of America, France
- Published
- 6 June 2024
- Authors
- Robin San Roman, Pierre Fernandez, Alexandre Défossez, Teddy Furon, Tuan Tran, Hady Elsahar
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Audio, Speech
- Task
- Audio classification, Audio generation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
4,500 hours * 13,680 English words per hour = 61560000 words
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Training code
- Open source
The code in this repository is released under the MIT license https://github.com/facebookresearch/audioseal training code: https://github.com/facebookresearch/audioseal/blob/main/docs/TRAINING.md looks like training scripts are in this repo: https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/solvers/watermark.py
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Proactive Detection of Voice Cloning with Localized Watermarking
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Audioseal was published by Facebook AI Research, in the country recorded as United States of America, during June 2024. It comes out of an organisation categorised as industry.
It works in the domain of Audio, Speech, and is recorded as performing the task of audio classification, Audio generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Audioseal — common questions
Audioseal— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Audioseal— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Audioseal— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Audioseal— who created it?
It was published by Facebook AI Research, based in United States of America, an organisation categorised as industry.
Audioseal— when was it released?
It was published in June 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Audioseal— what is it used for?
It works in the domain of Audio, Speech, and is recorded as handling the task of audio classification, Audio generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.