ESC-50 Dataset

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Visualization of the ESC-50 Dataset in the Deep Lake UI

ESC-50 dataset

What is ESC-50 Dataset?

The ESC-50 (Environmental Sound Classification 50) Dataset is a collection of 2000 audio recordings of an environment suitable for benchmarking methods of environmental sound classification. The dataset consists of 50 semantical classes with each having 5-second-long recordings and loosely organized into 5 major categories.

Download ESC-50 Dataset in Python

Instead of downloading the ESC-50 dataset in Python, you can effortlessly load it in Python via our Deep Lake open source with just one line of code.

Load ESC-50 Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/esc50")

ESC-50 Dataset Structure

ESC-50 Data Fields

audio: tensor containing audio data.
labels: tensor containing labels for the audio.
esc10: tensor containing boolean values of whether the data contains in esc10 dataset.
take: tensor containing the revision number of the audio.
fold: tensor containing the fold number of the audio.
target: tensor containing the id of the audio.
src_file: tensor containing the file name of the audio.

How to use ESC-50 Dataset with PyTorch and TensorFlow in Python

Train a model on the ESC-50 dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)

Train a model on the ESC-50 dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about ESC-50 Dataset

ESC-50 Dataset Description

Repository: https://github.com/karolpiczak/ESC-50
Paper: Piczak, K. J. (2015, October). ESC: Dataset for environmental sound classification. In Proceedings of the 23rd ACM international conference on Multimedia (pp. 1015-1018).

ESC-50 Dataset Curators

K. J. Piczak

ESC-50 Dataset Licensing Information

http://creativecommons.org/licenses/by-nc/3.0/

ESC-50 Dataset Citation Information

				
					@inproceedings{piczak2015dataset,
  title = {{ESC}: {Dataset} for {Environmental Sound Classification}},
  author = {Piczak, Karol J.},
  booktitle = {Proceedings of the 23rd {Annual ACM Conference} on {Multimedia}},
  date = {2015-10-13},
  url = {http://dl.acm.org/citation.cfm?doid=2733373.2806390},
  doi = {10.1145/2733373.2806390},
  location = {{Brisbane, Australia}},
  isbn = {978-1-4503-3459-4},
  publisher = {{ACM Press}},
  pages = {1015--1018}
}