Acoustic monitoringMusic and sound

EAT

A self-supervised sound understanding model that is markedly cheaper to train on your own data than its predecessors. The system does not make a diagnosis, it gives you a reason to check the unit before it fails.

Developer: Shanghai Jiao Tong University and Peng Cheng Laboratory, China
First release: Jan 2024
Latest release: May 2025
Sizes: 90M (base) and 309M (large)
License: Commercial use allowedMIT
Running: On your own serverAlso runs without a GPU
Industries: Manufacturing and logistics, Security, Science and research, Software development

Deploy on your server Hugging Face GitHub

What it does

Training your own audio model on modest hardware
Audio features for spotting abnormal operating modes
Labelling sound events on a site
Fine-tuning for the sounds of one production line

Where it is used

Manufacturing and equipment maintenanceSite securityResearch and developmentAudio archives

Hardware requirements

LaptopLaptop or regular PC, up to 8 GB of VRAM — smaller versions

fits

1 GPUOne GPU with 16–80 GB — mid-size versions

fits

ClusterServer with several GPUs — flagship versions

no versions

Versions

May 2025EAT-large и поддержка Hugging Face
Jan 2024EAT, статья и первые веса

How to run it

On your own serverWeights are downloaded from Hugging Face and served with vLLM (load and API) or llama.cpp (modest hardware). The model then runs in a closed network with no per-request fees.Hugging Face

How much hardware you needCalculate the VRAM for the model size, context length and number of concurrent requests.Open the hardware calculator

I can set this up end to end: pick the model size, deploy it on your server and connect it to your systems.

Frequently asked questions

Can EAT be used in a commercial project?

Yes. License: MIT. It allows commercial use, but it is still worth having a lawyer review the license before launch.

What hardware does EAT need?

At minimum: Laptop or regular PC, up to 8 GB of VRAM — smaller versions. Some versions also run on an ordinary CPU, without a GPU. You can calculate the exact VRAM for your model size and context in the hardware calculator.

Does EAT support Russian?

Language does not matter for this model: it does not work with text.

Where can I download EAT and what does it cost?

The EAT weights are open and free to download. You only pay for the hardware it runs on and for the setup. Source links are at the bottom of this page.

How I deploy it for clients

SelectionI pick the model size for your task and hardware and test it on your examples.
DeploymentI deploy it on your server or in a closed network and provide an API.
Fine-tuningI fine-tune it on your data (LoRA) or connect a knowledge base — whichever is cheaper for the task.
IntegrationI connect it to your CRM, ERP, bot, website or team chat and set up monitoring.

A self-supervised sound understanding model that became the base for many current systems: teams take it as a starting point and fine-tune it on their own equipment sounds. The system does not make a diagnosis, it gives you a reason to check the unit before it fails.

Details Acoustic monitoringOpenBEATsCarnegie Mellon University and co-authors (the ESPnet project) · USACommercial use allowed

A fully open reproduction of BEATs: code, training recipes and weights are all published. Teams pick it when they need a transparent base for their own sound model. The system does not make a diagnosis, it gives you a reason to check the unit before it fails.

Details Acoustic monitoringDASSMIT CSAIL and MIT-IBM Watson AI Lab · USACommercial use allowed

A sound understanding model built on state spaces instead of a transformer: it handles recordings hours long, which suits continuous listening to a production line. The system does not make a diagnosis, it gives you a reason to check the unit before it fails.

Details

Source: huggingface.co/worstchan/EAT-base_epoch30_finetune_AS2M. Data checked against the model card on 22 Sep 2026. Have a lawyer review the license before commercial launch.