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--- |
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dataset_info: |
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features: |
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- name: src_file |
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dtype: string |
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- name: fold |
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dtype: int64 |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': dog |
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'1': rooster |
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'2': pig |
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'3': cow |
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'4': frog |
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'5': cat |
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'6': hen |
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'7': insects |
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'8': sheep |
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'9': crow |
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'10': rain |
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'11': sea_waves |
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'12': crackling_fire |
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'13': crickets |
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'14': chirping_birds |
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'15': water_drops |
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'16': wind |
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'17': pouring_water |
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'18': toilet_flush |
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'19': thunderstorm |
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'20': crying_baby |
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'21': sneezing |
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'22': clapping |
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'23': breathing |
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'24': coughing |
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'25': footsteps |
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'26': laughing |
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|
'27': brushing_teeth |
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|
'28': snoring |
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|
'29': drinking_sipping |
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|
'30': door_wood_knock |
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'31': mouse_click |
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'32': keyboard_typing |
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'33': door_wood_creaks |
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'34': can_opening |
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'35': washing_machine |
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'36': vacuum_cleaner |
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'37': clock_alarm |
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'38': clock_tick |
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'39': glass_breaking |
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'40': helicopter |
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'41': chainsaw |
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'42': siren |
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'43': car_horn |
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'44': engine |
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'45': train |
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'46': church_bells |
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'47': airplane |
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'48': fireworks |
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'49': hand_saw |
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- name: esc10 |
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dtype: bool |
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|
- name: take |
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|
dtype: string |
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|
- name: audio |
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|
dtype: audio |
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|
splits: |
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|
- name: train |
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|
num_bytes: 882179256 |
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|
num_examples: 2000 |
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|
download_size: 773038488 |
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dataset_size: 882179256 |
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configs: |
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- config_name: default |
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data_files: |
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|
- split: train |
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|
path: data/train-* |
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license: cc-by-nc-2.0 |
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task_categories: |
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|
- audio-classification |
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size_categories: |
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|
- 1K<n<10K |
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--- |
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# Dataset Card for "esc50" |
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This is a mirror for the ESC-50 dataset. Original sources: |
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https://github.com/karolpiczak/ESC-50 |
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K. J. Piczak. ESC: Dataset for Environmental Sound Classification. Proceedings of the 23rd Annual ACM Conference on Multimedia, Brisbane, Australia, 2015. |
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[DOI: http://dx.doi.org/10.1145/2733373.2806390] |
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The dataset is available under the terms of the Creative Commons Attribution Non-Commercial license. |
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## Exploring the dataset |
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You can visualize the dataset using Renumics Spotlight: |
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```python |
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import datasets |
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from renumics import spotlight |
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ds = datasets.load_dataset('renumics/esc50', split='train') |
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spotlight.show(ds) |
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``` |
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## Explore enriched dataset |
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To fully understand the dataset, you can leverage model results such as embeddings or predictions. |
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Here is an example how to use zero-shot classification with MS CLAP for this purpose: |
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```python |
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ds_results = datasets.load_dataset("renumics/esc50-clap2023-results",split='train') |
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ds = datasets.concatenate_datasets([ds, ds_results], axis=1) |
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spotlight.show(ds, dtype={'text_embedding': spotlight.Embedding, 'audio_embedding': spotlight.Embedding}) |
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``` |
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