Baby Cry Classification Model
Classifies baby cries into 5 categories to help parents understand their baby's needs.
Categories
- 🍼 Hungry: Baby needs feeding
- 😴 Tired: Baby needs sleep
- 😣 Belly Pain: Digestive discomfort
- 💨 Burping: Needs burping
- 😢 Discomfort: General discomfort (diaper, temperature, etc.)
Model Details
- Base Model: HuBERT (facebook/hubert-base-ls960)
- Architecture: HuBERT + Custom Classification Head
- Sample Rate: 16kHz
- Input Duration: 5 seconds
- Framework: PyTorch + Transformers
Usage
import requests
import numpy as np
# Load audio file
audio_data = open("baby_cry.wav", "rb").read()
# Call API
API_URL = "https://api-inference.huggingface.co/models/dontcryai/baby-cry-classifier"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
response = requests.post(API_URL, headers=headers, data=audio_data)
result = response.json()
print(result)
# [{'label': 'hungry', 'score': 0.85}, ...]
Training
Trained on custom baby cry dataset with data augmentation.
License
MIT License - Free for commercial and personal use
- Downloads last month
- -