Datasets:
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---
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'}
``` |