MSEB Leaderboard

MSEB Leaderboard

A leaderboard page for the Massive Sound Embedding Benchmark, published by Google, built with Gradio. For researchers who compare sound embedding models.

Made by google on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: This is a Gradio leaderboard for the Massive Sound Embedding Benchmark, published by Google. The page's own description is a single line, so the details of the tasks and scoring are not given here. The repository holds a results folder and a source folder, and the Space runs on the basic CPU tier at Hugging Face.

Who it suits: Researchers who work with sound or speech embeddings and want to see how models rank, and developers who want a Gradio example of a leaderboard backed by stored result files.

Industry | Who uses it | How they use it | Example
Research | Audio and speech researchers | Look up how sound embedding models rank | Find where a model of interest sits
Software | Developers | Read app.py and src to see how results are shown | Reuse the layout for their own leaderboard
Academia | Benchmark users | Check a model's standing before choosing it | Compare two models from the table

Skip it if you need task descriptions or scoring rules, since the page gives none.

Where to use it:

• A web browser, on the Space's page at Hugging Face
• Your own computer, as a local copy, once Python 3.13 is installed

Use case | Who | What they get back
Check model rankings | Researchers | A leaderboard of results on the benchmark
Study the app | Developers | A Gradio app with a results folder to read

How to set it up:

• Nothing at all if you only use the online Space
• Python 3.13 and pip, for a local copy
• The Python packages listed in requirements.txt

1. Open the Space's page at Hugging Face to use it online.
2. For a local copy, install the Hugging Face hub package and download the Space.
3. Move into the new folder and install Gradio at the pinned version.
4. Install the packages from requirements.txt.
5. Start the app with Python.

pip install -U huggingface_hub
hf download google/mseb-leaderboard --repo-type space --local-dir mseb-leaderboard
cd mseb-leaderboard
pip install gradio==5.50.0
pip install -r requirements.txt
python app.py

The repository is large, at 1996 files, mostly under its results folder. The page does not say what a local run needs beyond the packages.

How to use it: Online, open the Space and read the leaderboard on its page. On your own machine, run the app file and open the page Gradio serves in your browser. The page lists no other commands.

Tool | What it does
python app.py | Starts the Gradio leaderboard on your computer

Example: You open the Space to see how sound embedding models compare on the benchmark. You look for the model you are considering and read how it ranks next to the others.

For newcomers and experts: If you are new, a sound embedding turns audio into numbers so that similar sounds sit close together. Open the Space online and read the table from the top.

If you know the field, the page does not list the tasks, metrics or how a model is added. For those details, look for the benchmark's own documentation before drawing conclusions from the ranks.

Open the Space in a browser
The app loads the stored results
The leaderboard is drawn
You read how the models rank

Good to know:

• License: apache-2
• Cost to run: none named
• Data it reaches: the stored result files in the repository

On Hugging Face, 13 November 2026

• Likes: 5
• Built with: gradio
• License: apache-2.0

Links

• Space: https://huggingface.co/spaces/google/mseb-leaderboard
• Files: https://huggingface.co/spaces/google/mseb-leaderboard/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.