hche11 / VGGSound

VGGSound: A Large-scale Audio-Visual Dataset

VGGSound

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

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

Dataset

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	\	✔️	AudioSet (common)	ASTest	0.286	0.899	1.803
B	VGGish	\	✔️	VGGSound (common)	ASTest	0.326	0.916	1.950
C	VGGish	\	❌	VGGSound (common)	ASTest	0.301	0.910	1.900
D	ResNet18	AveragePool	❌	VGGSound (common)	ASTest	0.328	0.923	2.024
E	ResNet18	NetVLAD	❌	VGGSound (common)	ASTest	0.369	0.927	2.058
F	ResNet18	AveragePool	❌	VGGSound	ASTest	0.404	0.944	2.253
G	ResNet18	NetVLAD	❌	VGGSound	ASTest	0.434	0.950	2.327
H	ResNet18	AveragePool	❌	VGGSound	VGGSound	0.516	0.968	2.627
I	ResNet18	NetVLAD	❌	VGGSound	VGGSound	0.512	0.970	2.660
J	ResNet34	AveragePool	❌	VGGSound	ASTest	0.409	0.947	2.292
K	ResNet34	AveragePool	❌	VGGSound	VGGSound	0.529	0.972	2.703
L	ResNet50	AveragePool	❌	VGGSound	ASTest	0.412	0.949	2.309
M	ResNet50	AveragePool	❌	VGGSound	VGGSound	0.532	0.973	2.735

Environment

• Python 3.6.8
• Pytorch 1.3.0

Pretrained model and evaluation

We provide the pretrained models H an I here,

wget http://www.robots.ox.ac.uk/~vgg/data/vggsound/models/H.pth.tar
wget http://www.robots.ox.ac.uk/~vgg/data/vggsound/models/I.pth.tar

To test the model and generate prediction files,

python test.py --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 eval.py --result_path "directory to predictions/"

Citation

@InProceedings{Chen20,
  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",
}

License

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.