README.md

metadata

language:
  - en
license: cc-by-4.0
size_categories:
  - 10K<n<100K
  - 1M<n<10M
source_datasets:
  - original
task_categories:
  - audio-classification
paperswithcode_id: audioset
pretty_name: AudioSet
config_names:
  - balanced
  - unbalanced
tags:
  - audio
dataset_info:
  - config_name: balanced
    features:
      - name: video_id
        dtype: string
      - name: audio
        dtype: audio
      - name: labels
        sequence: string
      - name: human_labels
        sequence: string
    splits:
      - name: train
        num_bytes: 26016210987
        num_examples: 18685
      - name: test
        num_bytes: 23763682278
        num_examples: 17142
    download_size: 49805654900
    dataset_size: 49779893265
  - config_name: unbalanced
    features:
      - name: video_id
        dtype: string
      - name: audio
        dtype: audio
      - name: labels
        sequence: string
      - name: human_labels
        sequence: string
    splits:
      - name: train
        num_bytes: 2408656417541
        num_examples: 1738788
      - name: test
        num_bytes: 23763682278
        num_examples: 17142
    download_size: 2433673104977
    dataset_size: 2432420099819

Dataset Card for AudioSet

Dataset Description

• Homepage: https://research.google.com/audioset/index.html
• Paper: https://storage.googleapis.com/gweb-research2023-media/pubtools/pdf/45857.pdf
• Leaderboard: https://paperswithcode.com/sota/audio-classification-on-audioset

Dataset Summary

AudioSet is a dataset of 10-second clips from YouTube, annotated into one or more sound categories, following the AudioSet ontology.

Supported Tasks and Leaderboards

• audio-classification: Classify audio clips into categories. The leaderboard is available here

Languages

The class labels in the dataset are in English.

Dataset Structure

Data Instances

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']
}

Data Fields

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.

Data Splits

The distribuion of audio clips is as follows:

balanced configuration

	train	test
# instances	18685	17142

unbalanced configuration

	train	test
# instances	1738788	17142

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

The labels are from the AudioSet ontology. Audio clips are from YouTube.

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

1. The YouTube videos in this copy of AudioSet were downloaded in March 2023, so not all of the original audios are available. The number of clips able to be downloaded is as follows:
   • Balanced train: 18685 audio clips out of 22160 originally.
   • Unbalanced train: 1738788 clips out of 2041789 originally.
   • Evaluation: 17142 audio clips out of 20371 originally.
2. Most audio is sampled at 48 kHz 24 bit, but about 10% is sampled at 44.1 kHz 24 bit. Audio files are stored in the FLAC format.

Additional Information

Dataset Curators

More Information Needed

Licensing Information

The AudioSet data is licensed under CC-BY-4.0

Citation

@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}
}