task_categories:audio-classification size_categories:1K<n<10K license:other region:us
---
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: 8774740.0
    num_examples: 12
  - name: validation
    num_bytes: 8774740.0
    num_examples: 12
  - name: test
    num_bytes: 8774740.0
    num_examples: 12
  download_size: 26037015
  dataset_size: 26324220.0
task_categories:
- audio-classification
size_categories:
- 1K<n<10K
---
# Gaepago (Gae8J/gaepago_s)
## How to use
### 1. Install dependencies
```bash
pip install datasets==2.10.1
pip install soundfile==0.12.1
pip install librosa==0.10.0.post2
```
### 2. Load the dataset
```python
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
```python
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'}
```