labhamlet/wavjepa-base
Feature Extraction • 0.2B • Updated • 1.48k • 2
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
AudioSet is a dataset of 10-second clips from YouTube, annotated into one or more sound categories, following the AudioSet ontology.
audio-classification: Classify audio clips into categories. The leaderboard is available hereThe class labels in the dataset are in English.
Example instance from the dataset:
{
'video_id': '--PJHxphWEs',
'audio': {
'path': 'audio/bal_train/--PJHxphWEs.flac',
'array': array([-0.04364824, -0.05268681, -0.0568949 , ..., 0.11446512,
0.14912748, 0.13409865]),
'sampling_rate': 48000
},
'labels': ['/m/09x0r', '/t/dd00088'],
'human_labels': ['Speech', 'Gush']
}
Instances have the following fields:
video_id: a string feature containing the original YouTube ID.audio: an Audio feature containing the audio data and sample rate.labels: a sequence of string features containing the labels
associated with the audio clip.human_labels: a sequence of string features containing the
human-readable forms of the same labels as in labels.The distribuion of audio clips is as follows:
balanced configuration
| train | test | |
|---|---|---|
| # instances | 18683 | 17141 |
unbalanced configuration
| train | test | |
|---|---|---|
| # instances | 1738657 | 17141 |
The labels are from the AudioSet ontology. Audio clips are from YouTube.
The AudioSet data is licensed under CC-BY-4.0
@inproceedings{jort_audioset_2017,
title = {Audio Set: An ontology and human-labeled dataset for audio events},
author = {Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter},
year = {2017},
booktitle = {Proc. IEEE ICASSP 2017},
address = {New Orleans, LA}
}