metadata

dataset_info:
  features:
    - name: src_file
      dtype: string
    - name: fold
      dtype: int64
    - name: label
      dtype:
        class_label:
          names:
            '0': dog
            '1': rooster
            '2': pig
            '3': cow
            '4': frog
            '5': cat
            '6': hen
            '7': insects
            '8': sheep
            '9': crow
            '10': rain
            '11': sea_waves
            '12': crackling_fire
            '13': crickets
            '14': chirping_birds
            '15': water_drops
            '16': wind
            '17': pouring_water
            '18': toilet_flush
            '19': thunderstorm
            '20': crying_baby
            '21': sneezing
            '22': clapping
            '23': breathing
            '24': coughing
            '25': footsteps
            '26': laughing
            '27': brushing_teeth
            '28': snoring
            '29': drinking_sipping
            '30': door_wood_knock
            '31': mouse_click
            '32': keyboard_typing
            '33': door_wood_creaks
            '34': can_opening
            '35': washing_machine
            '36': vacuum_cleaner
            '37': clock_alarm
            '38': clock_tick
            '39': glass_breaking
            '40': helicopter
            '41': chainsaw
            '42': siren
            '43': car_horn
            '44': engine
            '45': train
            '46': church_bells
            '47': airplane
            '48': fireworks
            '49': hand_saw
    - name: esc10
      dtype: bool
    - name: take
      dtype: string
    - name: audio
      dtype: audio
  splits:
    - name: train
      num_bytes: 882179256
      num_examples: 2000
  download_size: 773038488
  dataset_size: 882179256
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-nc-2.0
task_categories:
  - audio-classification
size_categories:
  - 1K<n<10K

Dataset Card for "esc50"

This is a mirror for the ESC-50 dataset. Original sources:

https://github.com/karolpiczak/ESC-50 K. J. Piczak. ESC: Dataset for Environmental Sound Classification. Proceedings of the 23rd Annual ACM Conference on Multimedia, Brisbane, Australia, 2015. [DOI: http://dx.doi.org/10.1145/2733373.2806390]

The dataset is available under the terms of the Creative Commons Attribution Non-Commercial license.

Exploring the dataset

You can visualize the dataset using Renumics Spotlight:

import datasets
from renumics import spotlight

ds = datasets.load_dataset('renumics/esc50', split='train')

spotlight.show(ds)

Explore enriched dataset

To fully understand the dataset, you can leverage model results such as embeddings or predictions.

Here is an example how to use zero-shot classification with MS CLAP for this purpose:

ds_results = datasets.load_dataset("renumics/esc50-clap2023-results",split='train')
ds = datasets.concatenate_datasets([ds, ds_results], axis=1)

spotlight.show(ds, dtype={'text_embedding': spotlight.Embedding, 'audio_embedding': spotlight.Embedding})