README.md

metadata

title: Baby Cry Ai
emoji: 😻
colorFrom: yellow
colorTo: indigo
sdk: gradio
sdk_version: 6.3.0
app_file: app.py
pinned: false
license: mit

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

👶 Baby Cry AI Service

A Flask microservice that analyzes baby cry audio to identify the reason for crying using an ensemble of Hugging Face models.

🎯 Features

• Ensemble Model Approach: Combines supervised and zero-shot classification for improved accuracy
• Single Load Architecture: Models loaded once at startup for optimal performance
• Docker Ready: Production-ready containerized deployment
• REST API: Simple POST endpoint for audio analysis

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                    Baby Cry AI Service                       │
├─────────────────────────────────────────────────────────────┤
│  app.py (Flask API)                                         │
│    └── POST /analyze-cry                                    │
│         └── inference.py                                    │
│              ├── Supervised Model (Wiam/baby-cry-*)         │
│              └── Zero-Shot Model (laion/clap-htsat-unfused) │
└─────────────────────────────────────────────────────────────┘

🚀 Quick Start

Local Development

# Install dependencies
pip install -r requirements.txt

# Run the service
python app.py

Docker

# Build image
docker build -t baby-cry-ai .

# Run container
docker run -p 5000:5000 baby-cry-ai

📡 API Reference

Health Check

GET /health

Response:

{
  "status": "healthy",
  "service": "baby-cry-ai"
}

Analyze Cry

POST /analyze-cry
Content-Type: multipart/form-data

Request:

• audio: Audio file (WAV, MP3, OGG, FLAC, M4A, WebM)

Response:

{
  "cry_detected": true,
  "top_reason": "hunger",
  "scores": {
    "hunger": 0.45,
    "belly_pain": 0.20,
    "tired": 0.15,
    "discomfort": 0.12,
    "burping": 0.08
  },
  "disclaimer": "AI-generated suggestion, not a medical diagnosis. Please consult a healthcare professional for medical advice."
}

Example Usage

# Using curl
curl -X POST http://localhost:5000/analyze-cry \
  -F "audio=@baby_cry.wav"

# Using Python requests
import requests

with open("baby_cry.wav", "rb") as f:
    response = requests.post(
        "http://localhost:5000/analyze-cry",
        files={"audio": f}
    )
    print(response.json())

🏷️ Cry Categories

Label	Description
hunger	Baby is hungry (rhythmic "neh" sound)
belly_pain	Stomach discomfort (sharp, high-pitched)
tired	Baby needs sleep (heavy, yawning cry)
discomfort	General discomfort (fussy, whiny)
burping	Needs to burp (repetitive sounds)

🧠 Models Used

1. Supervised Model: Wiam/baby-cry-classification-finetuned-babycry-v4

   • Fine-tuned specifically for baby cry classification

2. Zero-Shot Model: laion/clap-htsat-unfused

   • CLAP model for audio-text matching
   • Provides additional context via natural language prompts

📁 Project Structure

baby-cry-ai-service/
├── app.py              # Flask entry point
├── inference.py        # Model loading & inference logic
├── requirements.txt    # Python dependencies
├── Dockerfile          # Container configuration
├── .env.example        # Environment variables template
└── README.md           # Documentation

⚙️ Environment Variables

Variable	Default	Description
PORT	5000	Server port
FLASK_DEBUG	false	Enable debug mode

🔧 Production Deployment

For production, the Docker image uses Gunicorn with:

• Single worker (due to model memory requirements)
• 120s timeout for large audio files
• Health check endpoint

# Production run with custom port
docker run -p 8080:5000 -e PORT=5000 baby-cry-ai

⚠️ Disclaimer

This service provides AI-generated suggestions only and should NOT be used as a substitute for professional medical advice. Always consult with a healthcare professional for concerns about your baby's health.

📄 License

MIT License