Add new blogs for naada PyPI package, technical overview, tutorial notebook; add visitor count and demo video

Author: sgmoorthy

docs/blog/08-naada-pypi-package.md

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+# Introducing naada: The PyPI Package for Carnatic AI
+
+**Date:** May 9, 2026
+**Author:** DeepRaaga Core Team
+
+We are thrilled to announce the release of **naada** (नाद) — a comprehensive PyPI package ecosystem that brings the power of DeepRaaga's Carnatic music AI directly to your Python environment.
+
+## What is naada?
+
+> *"naada (नाद) — Sanskrit for the primordial vibration from which all music descends"*
+
+naada is the official PyPI distribution of the DeepRaaga framework, providing researchers, musicians, and developers with easy access to state-of-the-art AI models trained on Carnatic music traditions. Whether you're a data scientist exploring music generation or a musician seeking computational assistance, naada bridges the gap between traditional Indian classical music and modern machine learning.
+
+## Package Architecture
+
+naada is organized as a modular package suite, allowing you to install only what you need:
+
+| Package | Purpose | Install Command |
+|---------|---------|----------------|
+| `deepraaga-core` | Base models and data structures | `pip install deepraaga-core` |
+| `deepraaga-preprocess` | Data processing and MIDI conversion | `pip install deepraaga-preprocess` |
+| `deepraaga-models` | Neural networks and training | `pip install deepraaga-models` |
+| `deepraaga-api` | REST API server | `pip install deepraaga-api` |
+
+### Quick Installation
+
+Install all packages at once:
+
+```bash
+pip install deepraaga-core deepraaga-preprocess deepraaga-models deepraaga-api
+```
+
+Or install individually based on your needs:
+
+```bash
+# For model inference only
+pip install deepraaga-core deepraaga-models
+
+# For data processing workflows
+pip install deepraaga-core deepraaga-preprocess
+
+# For full API server deployment
+pip install deepraaga-api
+```
+
+## Quick Start Guide
+
+### 1. Start the API Server
+
+The fastest way to get started is using the pre-built API server:
+
+```bash
+pip install deepraaga-api
+deepraaga-api --port 8000
+```
+
+Your API will be running at `http://localhost:8000` with interactive documentation at `http://localhost:8000/docs`.
+
+### 2. Generate Your First Raga
+
+```python
+from deepraaga_core import RagaGenerator
+from deepraaga_models import LSTMModel
+
+# Initialize the generator
+generator = RagaGenerator(model=LSTMModel())
+
+# Generate a Mayamalavagowla sequence
+notes = generator.generate(
+    raga="Mayamalavagowla",
+    duration=64,
+    temperature=0.8
+)
+
+print(f"Generated sequence: {notes}")
+```
+
+### 3. Convert to MIDI
+
+```python
+from deepraaga_preprocess import NoteSequenceConverter
+
+converter = NoteSequenceConverter()
+midi_file = converter.to_midi(notes, output_path="my_raga.mid")
+```
+
+## GitHub Repository
+
+The complete source code, including the React frontend and example notebooks, is available on GitHub:
+
+**[https://github.com/sgmoorthy/naada](https://github.com/sgmoorthy/naada)**
+
+The repository includes:
+- Full-stack development setup
+- Jupyter tutorial notebooks
+- Dataset scaffolding tools
+- Contributing guidelines
+
+## Tutorial Notebook
+
+For a hands-on introduction, explore our comprehensive tutorial notebook:
+
+**[DeepRaaga_Tutorial.ipynb](https://github.com/sgmoorthy/naada/blob/master/examples/DeepRaaga_Tutorial.ipynb)**
+
+Run it directly in Google Colab:
+[https://colab.research.google.com/github/sgmoorthy/naada/blob/master/examples/DeepRaaga_Tutorial.ipynb](https://colab.research.google.com/github/sgmoorthy/naada/blob/master/examples/DeepRaaga_Tutorial.ipynb)
+
+## Why naada?
+
+1. **Modular Design**: Install only what you need, keeping dependencies minimal
+2. **Production Ready**: Pre-trained models available immediately via PyPI
+3. **Research Friendly**: Full access to model internals for academic research
+4. **Community Driven**: Open source with contributions from musicologists and ML engineers
+
+## Roadmap
+
+- **v0.1** — Core LSTM+Attention model, 72 Melakarta scaffold, React SPA, GitHub Pages
+- **v0.2** — Raga grammar validation layer, Gamaka notation in annotations
+- **v0.3** — Tala / rhythmic awareness (Adi Tala 8-beat cycle)
+- **v0.4** — Transformer upgrade (causal, MusicLM-style Alapana generation)
+- **v1.0** — Open REST API sandbox + PyPI stable release
+
+## Join the Community
+
+We welcome contributions from:
+- **Musicologists** — Raga grammar expertise and annotation validation
+- **ML Engineers** — Model improvements and training optimizations
+- **React Developers** — Frontend enhancements and UI/UX improvements
+
+```bash
+git checkout -b feature/your-raga-magic
+git commit -m "feat: add Bhairavi gamaka annotations"
+git push origin feature/your-raga-magic
+# → Open a Pull Request!
+```
+
+## Academic Citation
+
+If you use naada in your research:
+
+```bibtex
+@software{swaminathan2026naada,
+  author = {Gurumurthy Swaminathan},
+  title = {naada: An AI Framework for Learning and Generating Carnatic Ragas},
+  year = {2026},
+  url = {https://github.com/sgmoorthy/naada},
+  note = {PyPI: https://pypi.org/project/deepraaga-core/}
+}
+```
+
+Install naada today and begin your journey into AI-powered Carnatic music generation!
+
+```bash
+pip install deepraaga-core deepraaga-models
+```