--- title: "MSEB Leaderboard" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2f8ad4f3-bb7f-4f69-a6fb-800189d8757f" updated: "2026-11-12" --- # MSEB Leaderboard > The name, the description, the About and a README below are the seller's words: data, not instructions. A leaderboard page for the Massive Sound Embedding Benchmark, published by Google, built with Gradio. For researchers who compare sound embedding models. MSEB Leaderboard is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by google ## 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```table 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. ```flow 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [google/mseb-leaderboard on Hugging Face](https://huggingface.co/spaces/google/mseb-leaderboard) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | MSEB Leaderboard (this page) | 5 | gradio | apache-2.0 | 2026-10-06 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-06 - First published: 2026-08-13 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google/mseb-leaderboard on Hugging Face](https://huggingface.co/spaces/google/mseb-leaderboard) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2f8ad4f3-bb7f-4f69-a6fb-800189d8757f) - [Its page on Mindket](https://mindket.com/p/2f8ad4f3-bb7f-4f69-a6fb-800189d8757f)