Datasets:
src_file
stringlengths 14
17
| fold
int64 1
5
| label
class label 50
classes | esc10
bool 2
classes | take
stringclasses 8
values | audio
audioduration (s) 5
5
|
---|---|---|---|---|---|
1-100032-A-0.wav | 1 | 0dog
| true | A | |
1-100038-A-14.wav | 1 | 14chirping_birds
| false | A | |
1-100210-A-36.wav | 1 | 36vacuum_cleaner
| false | A | |
1-100210-B-36.wav | 1 | 36vacuum_cleaner
| false | B | |
1-101296-A-19.wav | 1 | 19thunderstorm
| false | A | |
1-101296-B-19.wav | 1 | 19thunderstorm
| false | B | |
1-101336-A-30.wav | 1 | 30door_wood_knock
| false | A | |
1-101404-A-34.wav | 1 | 34can_opening
| false | A | |
1-103298-A-9.wav | 1 | 9crow
| false | A | |
1-103995-A-30.wav | 1 | 30door_wood_knock
| false | A | |
1-103999-A-30.wav | 1 | 30door_wood_knock
| false | A | |
1-104089-A-22.wav | 1 | 22clapping
| false | A | |
1-104089-B-22.wav | 1 | 22clapping
| false | B | |
1-105224-A-22.wav | 1 | 22clapping
| false | A | |
1-110389-A-0.wav | 1 | 0dog
| true | A | |
1-110537-A-22.wav | 1 | 22clapping
| false | A | |
1-115521-A-19.wav | 1 | 19thunderstorm
| false | A | |
1-115545-A-48.wav | 1 | 48fireworks
| false | A | |
1-115545-B-48.wav | 1 | 48fireworks
| false | B | |
1-115545-C-48.wav | 1 | 48fireworks
| false | C | |
1-115546-A-48.wav | 1 | 48fireworks
| false | A | |
1-115920-A-22.wav | 1 | 22clapping
| false | A | |
1-115920-B-22.wav | 1 | 22clapping
| false | B | |
1-115921-A-22.wav | 1 | 22clapping
| false | A | |
1-116765-A-41.wav | 1 | 41chainsaw
| true | A | |
1-11687-A-47.wav | 1 | 47airplane
| false | A | |
1-118206-A-31.wav | 1 | 31mouse_click
| false | A | |
1-118559-A-17.wav | 1 | 17pouring_water
| false | A | |
1-119125-A-45.wav | 1 | 45train
| false | A | |
1-121951-A-8.wav | 1 | 8sheep
| false | A | |
1-12653-A-15.wav | 1 | 15water_drops
| false | A | |
1-12654-A-15.wav | 1 | 15water_drops
| false | A | |
1-12654-B-15.wav | 1 | 15water_drops
| false | B | |
1-13571-A-46.wav | 1 | 46church_bells
| false | A | |
1-13572-A-46.wav | 1 | 46church_bells
| false | A | |
1-13613-A-37.wav | 1 | 37clock_alarm
| false | A | |
1-137-A-32.wav | 1 | 32keyboard_typing
| false | A | |
1-137296-A-16.wav | 1 | 16wind
| false | A | |
1-14262-A-37.wav | 1 | 37clock_alarm
| false | A | |
1-155858-A-25.wav | 1 | 25footsteps
| false | A | |
1-155858-B-25.wav | 1 | 25footsteps
| false | B | |
1-155858-C-25.wav | 1 | 25footsteps
| false | C | |
1-155858-D-25.wav | 1 | 25footsteps
| false | D | |
1-155858-E-25.wav | 1 | 25footsteps
| false | E | |
1-155858-F-25.wav | 1 | 25footsteps
| false | F | |
1-15689-A-4.wav | 1 | 4frog
| false | A | |
1-15689-B-4.wav | 1 | 4frog
| false | B | |
1-160563-A-48.wav | 1 | 48fireworks
| false | A | |
1-160563-B-48.wav | 1 | 48fireworks
| false | B | |
1-16568-A-3.wav | 1 | 3cow
| false | A | |
1-16746-A-15.wav | 1 | 15water_drops
| false | A | |
1-17092-A-27.wav | 1 | 27brushing_teeth
| false | A | |
1-17092-B-27.wav | 1 | 27brushing_teeth
| false | B | |
1-17124-A-43.wav | 1 | 43car_horn
| false | A | |
1-17150-A-12.wav | 1 | 12crackling_fire
| true | A | |
1-172649-A-40.wav | 1 | 40helicopter
| true | A | |
1-172649-B-40.wav | 1 | 40helicopter
| true | B | |
1-172649-C-40.wav | 1 | 40helicopter
| true | C | |
1-172649-D-40.wav | 1 | 40helicopter
| true | D | |
1-172649-E-40.wav | 1 | 40helicopter
| true | E | |
1-172649-F-40.wav | 1 | 40helicopter
| true | F | |
1-17295-A-29.wav | 1 | 29drinking_sipping
| false | A | |
1-17367-A-10.wav | 1 | 10rain
| true | A | |
1-17565-A-12.wav | 1 | 12crackling_fire
| true | A | |
1-17585-A-7.wav | 1 | 7insects
| false | A | |
1-17742-A-12.wav | 1 | 12crackling_fire
| true | A | |
1-17808-A-12.wav | 1 | 12crackling_fire
| true | A | |
1-17808-B-12.wav | 1 | 12crackling_fire
| true | B | |
1-1791-A-26.wav | 1 | 26laughing
| false | A | |
1-17970-A-4.wav | 1 | 4frog
| false | A | |
1-18074-A-6.wav | 1 | 6hen
| false | A | |
1-18074-B-6.wav | 1 | 6hen
| false | B | |
1-181071-A-40.wav | 1 | 40helicopter
| true | A | |
1-181071-B-40.wav | 1 | 40helicopter
| true | B | |
1-18527-A-44.wav | 1 | 44engine
| false | A | |
1-18527-B-44.wav | 1 | 44engine
| false | B | |
1-18631-A-23.wav | 1 | 23breathing
| false | A | |
1-18655-A-31.wav | 1 | 31mouse_click
| false | A | |
1-187207-A-20.wav | 1 | 20crying_baby
| true | A | |
1-18755-A-4.wav | 1 | 4frog
| false | A | |
1-18755-B-4.wav | 1 | 4frog
| false | B | |
1-18757-A-4.wav | 1 | 4frog
| false | A | |
1-18810-A-49.wav | 1 | 49hand_saw
| false | A | |
1-19026-A-43.wav | 1 | 43car_horn
| false | A | |
1-19111-A-24.wav | 1 | 24coughing
| false | A | |
1-19118-A-24.wav | 1 | 24coughing
| false | A | |
1-19501-A-7.wav | 1 | 7insects
| false | A | |
1-196660-A-8.wav | 1 | 8sheep
| false | A | |
1-196660-B-8.wav | 1 | 8sheep
| false | B | |
1-19840-A-36.wav | 1 | 36vacuum_cleaner
| false | A | |
1-19872-A-36.wav | 1 | 36vacuum_cleaner
| false | A | |
1-19872-B-36.wav | 1 | 36vacuum_cleaner
| false | B | |
1-19898-A-41.wav | 1 | 41chainsaw
| true | A | |
1-19898-B-41.wav | 1 | 41chainsaw
| true | B | |
1-19898-C-41.wav | 1 | 41chainsaw
| true | C | |
1-20133-A-39.wav | 1 | 39glass_breaking
| false | A | |
1-202111-A-3.wav | 1 | 3cow
| false | A | |
1-20545-A-28.wav | 1 | 28snoring
| false | A | |
1-20736-A-18.wav | 1 | 18toilet_flush
| false | A | |
1-208757-A-2.wav | 1 | 2pig
| false | A |
End of preview. Expand
in Dataset Viewer.
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})
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Size of downloaded dataset files:
773 MB
Size of the auto-converted Parquet files:
773 MB
Number of rows:
2,000