Massive Sound Embedding Benchmark (MSEB)
This is not an officially supported Google product.
The Massive Sound Embedding Benchmark (MSEB) provides a framework for assessing sound embedding methods across diverse sound categories and tasks that leverage improved sound representations.
The published leaderboard can be found at: https://google-research.github.io/mseb/leaderboard.html
Release files for mseb 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mseb-0.1.0.tar.gz | 5.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mseb-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 12.4 MB
Release files / mseb-0.1.0.tar.gz
| Download URL | mseb-0.1.0.tar.gz |
|---|---|
| Size | 5.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.3
|
Release files / mseb-0.1.0-py3-none-any.whl
| Download URL | mseb-0.1.0-py3-none-any.whl |
|---|---|
| Size | 7.0 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.3
|
