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Code and results for ICASSP2020 "VGGSound: A Large-scale Audio-Visual Dataset".

The repo contains the dataset file and our best audio classification model.


To download VGGSound, we provide a csv file. For each YouTube video, we provide YouTube URLs, time stamps, audio labels and train/test split. Each line in the csv file has columns defined by here.

# YouTube ID, start seconds, label,train/test split. 

A helpful link for data download!

Audio classification

We detail the audio classfication results here.

  • Pretrain refers whether the model was pretrained on YouTube-8M dataset.
  • Dataset (common) means it is a subset of the dataset. This subset only contains data of common classes (listed here) between AudioSet and VGGSound.
  • ASTest is the intersection of AudioSet and VGGSound testsets.
Model Aggregation Pretrain Finetune/Train Test mAP AUC d-prime
A VGGish \ :heavy_check_mark: AudioSet (common) ASTest 0.286 0.899 1.803
B VGGish \ :heavy_check_mark: VGGSound (common) ASTest 0.326 0.916 1.950
C VGGish \ :x: VGGSound (common) ASTest 0.301 0.910 1.900
D ResNet18 AveragePool :x: VGGSound (common) ASTest 0.328 0.923 2.024
E ResNet18 NetVLAD :x: VGGSound (common) ASTest 0.369 0.927 2.058
F ResNet18 AveragePool :x: VGGSound ASTest 0.404 0.944 2.253
G ResNet18 NetVLAD :x: VGGSound ASTest 0.434 0.950 2.327
H ResNet18 AveragePool :x: VGGSound VGGSound 0.516 0.968 2.627
I ResNet18 NetVLAD :x: VGGSound VGGSound 0.512 0.970 2.660
J ResNet34 AveragePool :x: VGGSound ASTest 0.409 0.947 2.292
K ResNet34 AveragePool :x: VGGSound VGGSound 0.529 0.972 2.703
L ResNet50 AveragePool :x: VGGSound ASTest 0.412 0.949 2.309
M ResNet50 AveragePool :x: VGGSound VGGSound 0.532 0.973 2.735


  • Python 3.6.8
  • Pytorch 1.3.0

Pretrained model and evaluation

We provide the pretrained model H and model I here.

To test the model and generate prediction files,

python --data_path "directory to audios/" --result_path "directory to predictions/" --summaries "path to pretrained models" --pool "avgpool"

To evaluate the model performance using the generated prediction files,

python --result_path "directory to predictions/"


  author       = "Honglie Chen and Weidi Xie and Andrea Vedaldi and Andrew Zisserman",
  title        = "VGGSound: A Large-scale Audio-Visual Dataset",
  booktitle    = "International Conference on Acoustics, Speech, and Signal Processing (ICASSP)",
  year         = "2020",


The VGG-Sound dataset is available to download for commercial/research purposes under a Creative Commons Attribution 4.0 International License. The copyright remains with the original owners of the video. A complete version of the license can be found here.

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