Datasets:
File size: 3,119 Bytes
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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:
```python
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:
```python
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})
```
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