Machine Learning Datasets Machine Learning Datasets
  • GitHub
  • Slack
  • Documentation
Get Started
Machine Learning Datasets Machine Learning Datasets
Get Started
Machine Learning Datasets
  • GitHub
  • Slack
  • Documentation

Docy

Machine Learning Datasets

  • Folder icon closed Folder open iconDatasets
    • MNIST
    • ImageNet Dataset
    • COCO Dataset
    • CIFAR 10 Dataset
    • CIFAR 100 Dataset
    • FFHQ Dataset
    • Places205 Dataset
    • GTZAN Genre Dataset
    • GTZAN Music Speech Dataset
    • The Street View House Numbers (SVHN) Dataset
    • Caltech 101 Dataset
    • LibriSpeech Dataset
    • dSprites Dataset
    • PUCPR Dataset
    • RAVDESS Dataset
    • GTSRB Dataset
    • CSSD Dataset
    • ATIS Dataset
    • Free Spoken Digit Dataset (FSDD)
    • not-MNIST Dataset
    • ECSSD Dataset
    • COCO-Text Dataset
    • CoQA Dataset
    • FGNET Dataset
    • ESC-50 Dataset
    • GlaS Dataset
    • UTZappos50k Dataset
    • Pascal VOC 2012 Dataset
    • Pascal VOC 2007 Dataset
    • Omniglot Dataset
    • HMDB51 Dataset
    • Chest X-Ray Image Dataset
    • NIH Chest X-ray Dataset
    • Fashionpedia Dataset
    • DRIVE Dataset
    • Kaggle Cats & Dogs Dataset
    • Lincolnbeet Dataset
    • Sentiment-140 Dataset
    • MURA Dataset
    • LIAR Dataset
    • Stanford Cars Dataset
    • SWAG Dataset
    • HASYv2 Dataset
    • WFLW Dataset
    • Visdrone Dataset
    • 11k Hands Dataset
    • QuAC Dataset
    • LFW Deep Funneled Dataset
    • LFW Funneled Dataset
    • Office-Home Dataset
    • LFW Dataset
    • PlantVillage Dataset
    • Optical Handwritten Digits Dataset
    • UCI Seeds Dataset
    • STN-PLAD Dataset
    • FER2013 Dataset
    • Adience Dataset
    • PPM-100 Dataset
    • CelebA Dataset
    • Fashion MNIST Dataset
    • Google Objectron Dataset
    • CARPK Dataset
    • CACD Dataset
    • Flickr30k Dataset
    • Kuzushiji-Kanji (KKanji) dataset
    • KMNIST
    • EMNIST Dataset
    • USPS Dataset
    • MARS Dataset
    • HICO Classification Dataset
    • NSynth Dataset
    • RESIDE dataset
    • Electricity Dataset
    • DRD Dataset
    • Caltech 256 Dataset
    • AFW Dataset
    • PACS Dataset
    • TIMIT Dataset
    • KTH Actions Dataset
    • WIDER Face Dataset
    • WISDOM Dataset
    • DAISEE Dataset
    • WIDER Dataset
    • LSP Dataset
    • UCF Sports Action Dataset
    • Wiki Art Dataset
    • FIGRIM Dataset
    • ANIMAL (ANIMAL10N) Dataset
    • OPA Dataset
    • DomainNet Dataset
    • HAM10000 Dataset
    • Tiny ImageNet Dataset
    • Speech Commands Dataset
    • 300w Dataset
    • Food 101 Dataset
    • VCTK Dataset
    • LOL Dataset
    • AQUA Dataset
    • LFPW Dataset
    • ARID Video Action dataset
    • NABirds Dataset
    • SQuAD Dataset
    • ICDAR 2013 Dataset
    • Animal Pose Dataset
  • Folder icon closed Folder open iconDeep Lake Docs Home
  • Folder icon closed Folder open iconDataset Visualization
  • API Basics
  • Storage & Credentials
  • Getting Started
  • Tutorials (w Colab)
  • Playbooks
  • Data Layout
  • Folder icon closed Folder open iconShuffling in ds.pytorch()
  • Folder icon closed Folder open iconStorage Synchronization
  • Folder icon closed Folder open iconTensor Relationships
  • Folder icon closed Folder open iconQuickstart
  • Folder icon closed Folder open iconHow to Contribute

ESC-50 Dataset

Estimated reading: 2 minutes

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}
}
				
			
Datasets - Previous FGNET Dataset Next - Datasets GlaS Dataset
Leaf Illustration

© 2022 All Rights Reserved by Snark AI, inc dba Activeloop