Loie(0219): create README.md

README.md

@@ -0,0 +1,48 @@
+---
+task_categories:
+- audio-classification
+size_categories:
+- 100B<n<1T
+---
+
+
+# VGGSound
+VGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube.
+
+
+- **Homepage:** https://www.robots.ox.ac.uk/~vgg/data/vggsound/
+- **Paper:** https://arxiv.org/abs/2004.14368
+
+## Analysis
+
+- **310+ classes:** VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications.
+- **200,000+ videos:** All videos are captured "in the wild" with audio-visual correspondence in the sense that the sound source is visually evident.
+- **550+ hours:** VGG-Sound consists of both audio and video. Each segment is 10 seconds long.
+
+![](src/data.png)
+ 
+
+## Download
+
+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. 
+```
+
+And you can download VGGSound directly from this [repository](https://huggingface.co/datasets/Loie/VGGSound/tree/main).
+
+
+## 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](https://thor.robots.ox.ac.uk/datasets/vggsound/license_vggsound.txt).
+
+## Citation Information
+Please cite the following if you make use of the dataset.
+```
+@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",
+}
+```