Buckets:
12.5 GB
37 files
Updated 7 days ago
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| Name | Size | Uploaded | Xet hash |
|---|---|---|---|
| licenses | 9 items | ||
| music | 10 items | ||
| noise | 2 items | ||
| speech | 14 items | ||
| .gitattributes | 2.5 kB xet | 738f1125 | |
| README.md | 3.09 kB xet | 59b5cae9 |
MUSAN (OpenSLR SLR17) is a corpus of music, speech, and noise recordings — ~109 hours of 16kHz mono audio assembled for training speech-processing systems such as VAD, speaker ID, and noise augmentation. The default config is the full labelled corpus for music/speech/noise discrimination; each subcorpus is also its own config so you can fetch just one:
music— ~42h across the FMA, HD-classical, Jamendo, and RFM sources.speech— ~60h of read speech (LibriVox, 12 languages) plus US government recordings.noise— ~6h of technical and ambient noise (free-sound, sound-bible).
from datasets import load_dataset
musan = load_dataset('corypaik/musan', split='train') # full labelled set
noise = load_dataset('corypaik/musan', 'noise', split='train') # one subcorpus
License
CC BY 4.0 overall (per the OpenSLR listing), but licensing is mixed by
source: free-sound noise is US public domain, sound-bible noise is assorted
Creative Commons, and the music and speech sources carry their own terms.
Each subcorpus ships a LICENSE mapping every clip to its terms; those files
are preserved under licenses/ in this repo.
Homepage: https://www.openslr.org/17/
Citation
@misc{snyder2015musan,
title={MUSAN: A Music, Speech, and Noise Corpus},
author={David Snyder and Guoguo Chen and Daniel Povey},
year={2015},
eprint={1510.08484},
archivePrefix={arXiv},
primaryClass={cs.SD},
}
- Total size
- 12.5 GB
- Files
- 37
- Last updated
- Oct 29
- Pre-warmed CDN
- US EU US EU