🎶 Swara Detection from Classical Music Notations (TFLite + Streamlit)

This project is a Streamlit-based web interface for detecting Swara (musical notes) from images of Indian classical music notations written in Devanagari script. It integrates a custom-trained TensorFlow Lite (TFLite) model, enabling real-time object detection of swaras directly from scanned book pages like the Kramik Pustak Vol. 2.

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📁 Project Structure

swaraDetectionUIUXWorking/ ├── results/ │ ├── labelmap.txt # Stores class label mappings │ └── result/ # Folder for storing detection outputs │ ├── tfLiteFile/ │ ├── interfaceForMacOs.py # Main Streamlit interface │ ├── labelmap.txt # Label mapping used by the model │ ├── trainedcom.tflite # Trained TFLite object detection model │ ├── requirements.txt # Python package dependencies └── README.md # Project documentation

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🚀 How It Works

1. Upload an image (JPG/PNG) of handwritten or printed classical notation.
2. Run detection: The TFLite model detects Swaras and draws bounding boxes.
3. Results displayed with confidence scores and overlaid annotations.
4. Output saved in a text file under results/.

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🛠️ Tech Stack

• TensorFlow Lite — lightweight object detection
• Streamlit — for building the UI
• OpenCV — image processing
• Label Studio — dataset annotation
• Python 3.9

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🧠 Model Details

• Architecture: SSD MobileNet V2 FPNlite 320
• Input Size: 320x320
• Classes: Multiple Swara classes including Sa, Re, Ga, Ma, etc.
• Training Data: Annotated swara snippets from Kramik Pustak Vol. 2
• Accuracy: Up to 98% on test data (avg: ~78%)

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▶️ Run Locally

Step 1: Clone this repo

git clone https://github.com/your-repo/swara-detection-ui.git
cd swara-detection-ui

### Step 2: Install dependencies  
```bash
pip install -r requirements.txt

Step 3: Launch Streamlit app

streamlit run tfLiteFile/interfaceForMacOs.py

📦 Requirements

Make sure your requirements.txt includes:

tensorflow==2.8.0 streamlit opencv-python numpy matplotlib Pillow

🤝 Contributors

Arupa Nanda Swain

A. Anushruth Reddy

C. Viswanath

Vadali SS Bharadwaja

📚 Acknowledgment

Special thanks to Pandit Vishnu Narayan Bhatkhande’s Kramik Pustak Vol. 2 — a foundational resource for classical notation.