metadata

license: other
dataset_info:
  features:
    - name: file
      dtype: string
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
    - name: label
      dtype:
        class_label:
          names:
            '0': bark
            '1': bow-wow
            '2': growling
            '3': howl
            '4': whimper
            '5': yip
    - name: is_unknown
      dtype: bool
    - name: youtube_id
      dtype: string
    - name: youtube_url
      dtype: string
  splits:
    - name: train
      num_bytes: 8774740
      num_examples: 12
    - name: validation
      num_bytes: 8774740
      num_examples: 12
    - name: test
      num_bytes: 8774740
      num_examples: 12
  download_size: 26037015
  dataset_size: 26324220
task_categories:
  - audio-classification
size_categories:
  - 1K<n<10K

Gaepago (Gae8J/gaepago_s)

How to use

1. Install dependencies

pip install datasets==2.10.1
pip install soundfile==0.12.1
pip install librosa==0.10.0.post2

2. Load the dataset

from datasets import load_dataset

dataset = load_dataset("Gae8J/gaepago_s")

Outputs

DatasetDict({
    train: Dataset({
        features: ['file', 'audio', 'label', 'is_unknown', 'youtube_id'],
        num_rows: 12
    })
    validation: Dataset({
        features: ['file', 'audio', 'label', 'is_unknown', 'youtube_id'],
        num_rows: 12
    })
    test: Dataset({
        features: ['file', 'audio', 'label', 'is_unknown', 'youtube_id'],
        num_rows: 12
    })
})

3. Check a sample

dataset['train'][0]

Outputs

{'file': 'bark/1_Q80fDGLRM.wav', 'audio': {'path': 'bark/1_Q80fDGLRM.wav', 'array': array([-9.15838356e-08,  6.80501699e-08,  1.97052145e-07, ...,
        0.00000000e+00,  0.00000000e+00,  0.00000000e+00]), 'sampling_rate': 16000}, 'label': 0, 'is_unknown': False, 'youtube_id': '1_Q80fDGLRM'}