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README.md

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+<div align="center">
+
+<!-- Laila Logo -->
+<img src="https://github.com/user-attachments/assets/560b6d36-7aef-4d72-bcac-1171fcb9e8c9" alt="Laila Logo" width="300"/>
+
+### Parameter-Efficient Adaptation of Whisper-Small for Indian-Accented and Code-Switched Hindi-English Speech
+
+[![GitHub](https://img.shields.io/badge/GitHub-techindro-black?logo=github)](https://github.com/techindro)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![Python 3.10+](https://img.shields.io/badge/Python-3.10+-blue?logo=python)](https://www.python.org/downloads/)
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-red?logo=pytorch)](https://pytorch.org/)
+[![Hugging Face](https://img.shields.io/badge/🤗-Transformers-orange)](https://huggingface.co/docs/transformers)
+[![PEFT](https://img.shields.io/badge/PEFT-LoRA-purple)](https://github.com/huggingface/peft)
+[![NeurIPS](https://img.shields.io/badge/NeurIPS-2026-blueviolet)](https://neurips.cc)
+
+**3.5M trainable params · 1.5% of base model · Trains on 1 free T4 GPU**
+
+</div>
+
 ---
-license: apache-2.0
+
+## 🌐 Platform Overview
+
+> *"Whisper, but for India — parameter-efficient ASR for Indian-accented and code-switched speech"*
+
+Laila provides high-quality speech recognition, model evaluation, and AI tools designed for Indian users, enterprises, and researchers working on next-generation voice applications.
+
 ---
+
+## 🚀 Live Demos
+
+Experience Laila in action. All demos run on Hugging Face Spaces — no installation required.
+
+| Demo | Description | Link |
+|------|-------------|------|
+| 🎙️ Hindi ASR | Hindi speech recognition with LoRA-tuned Whisper | [Try Now](https://huggingface.co/spaces/techindro/Laila-Demo) |
+| 🔀 Hinglish ASR | Code-switched Hindi-English transcription | [Try Now](https://huggingface.co/spaces/techindro/Laila-Hinglish) |
+| 🇮🇳 Indian English ASR | Indian-accented English transcription | [Try Now](https://huggingface.co/spaces/techindro/Laila-IndianEnglish) |
+| 🌐 22-Language Suite | One adapter per scheduled Indian language | [Coming Soon](https://huggingface.co/techindro) |
+
+🌐 Repository: [github.com/techindro/Laila](https://github.com/techindro/Laila)
+
+---
+
+## 📖 What is Laila?
+
+Laila is a **parameter-efficient speech recognition model** purpose-built for India's linguistic diversity. It adapts the base Whisper-Small model using LoRA, training only **1.5% of parameters** while keeping all 244M base weights frozen.
+
+### The Name
+
+- **Laila** = "Night" in Arabic/Persian, "play" in Hindi — evoking speech and expression
+- **Model** = Speech recognition for Indian languages
+
+So Laila means bringing clarity to Indian speech, day or night.
+
+### What Problem Does It Solve?
+
+Global speech models like Whisper, trained mostly on Western English, make more errors on Indian speech because:
+
+1. Indian-accented English has different phonetics, rhythm, and stress patterns
+2. Everyday speech is often code-switched (Hindi + English in one sentence)
+3. No single accepted transcript convention exists (Devanagari vs. Roman)
+4. Full fine-tuning is costly — needs more memory and produces full model copies
+
+**The result:** Speech recognition that works perfectly in San Francisco but struggles in Mumbai, Delhi, and Chennai.
+
+### How Does Laila Work?
+
+The pipeline has five parts that work together:
+
+| Part | What It Does |
+|------|---------------|
+| 🎧 Audio Preprocessing | Resampling, mono conversion, normalization, 30s chunking |
+| 🧠 Whisper-Small | Frozen 244M-parameter encoder-decoder Transformer |
+| 🔗 LoRA Adapters | Trains 3.5M params on `q_proj` & `v_proj` attention layers |
+| 🔊 Noise Augmentation | Mixes real background noise at random SNRs (train only) |
+| 📝 Transcript Convention | Devanagari for Hindi, Latin for English, normalized scoring |
+
+---
+
+## 📊 Key Results at a Glance
+
+| Metric | Laila | Whisper-Small (zero-shot) | Improvement |
+|--------|-------|---------------------------|-------------|
+| Trainable Parameters | 3.5M | 244M | **1.5% of base** |
+| Adapter Size | ~few MB | 244M | **~70× smaller** |
+| Hardware | 1× T4 (16GB) | 1× T4 (16GB) | **Free-tier** |
+| Hindi WER | TBD | TBD | TBD |
+| Indian-English WER | TBD | TBD | TBD |
+| Code-Switched WER | TBD | TBD | TBD |
+
+---
+
+## 🎯 The Problem
+
+**Global ASR training data ignores 1.4 billion Indian voices.**
+
+Existing models like Whisper were trained on 680k hours of mostly English data:
+
+| Limitation | Consequence |
+|------------|-------------|
+| Western English bias | Errors on Indian-accented English |
+| Monolingual training | Fails on code-switched speech |
+| Mixed-script ambiguity | Inconsistent training/evaluation |
+| Full fine-tuning cost | High memory, full model copies per variant |
+
+---
+
+## 🚀 The Solution
+
+Laila is a **parameter-efficient adaptation** of Whisper-Small purpose-built for India's linguistic diversity.
+
+### Comparison with Baselines
+
+| Feature | Whisper-Small (zero-shot) | Whisper-Small (full FT) | Laila (LoRA) |
+|---------|---------------------------|--------------------------|--------------|
+| Hindi Support | Partial | ✅ Yes | ✅ Yes |
+| Indian-Accented English | Partial | ✅ Yes | ✅ Yes |
+| Code-Switched (Hinglish) | ❌ | ✅ Yes | ✅ Yes |
+| Trainable Params | 0 | 244M | **3.5M** |
+| Adapter Size | — | Full model | **Few MB** |
+| Memory (Training) | — | High | **16GB T4** |
+| Base Model Weights | Frozen | Fully trained | **Frozen** |
+
+---
+
+## 🧠 AI Knowledge Hub
+
+Laila explores core pillars of modern speech AI and how LoRA applies each discipline to solve critical language challenges.
+
+### Core AI Disciplines
+
+**Parameter-Efficient Fine-Tuning** — *Train less. Do more.*
+> LoRA writes the update to a weight matrix as the product of two low-rank matrices and trains only these. This lowers memory use and the size of the stored update. Laila leverages LoRA to adapt Whisper-Small with just 1.5% of trainable parameters.
+- 3.5M trainable params vs. 244M base
+- Single free-tier T4 GPU
+- Adapter file of a few MB
+
+**Code-Switched Speech** — *Mixed language. Unified model.*
+> Code-switched speech mixes languages within a sentence — e.g., "kal meeting ke baad report bhej dena." Laila handles Hindi grammar with English nouns using separate test sets and a fixed transcript convention.
+- Separate Hindi, English, and code-switched test sets
+- Devanagari + Latin script convention
+- Noise-robust training
+
+---
+
+## 📂 Case Studies & Resources
+
+### Highlighted Case Studies
+
+| Case Study | Category | Description |
+|------------|----------|-------------|
+| **Hindi ASR with LoRA** | ASR | Adapting Whisper-Small for Hindi speech on a single free T4 GPU |
+| **Hinglish Code-Switching** | Code-Switching | Building a transcription pipeline for Hindi-English mixed speech |
+| **Noise-Robust ASR** | Robustness | Training with background noise for real-world conditions |
+
+### Learning Resources
+
+| Type | Title |
+|------|-------|
+| Paper | Laila: Parameter-Efficient Adaptation of Whisper-Small |
+| Guide | Guide to LoRA Fine-Tuning |
+| Guide | Guide: Whisper Architecture |
+| Guide | Guide: WER & CER Evaluation |
+
+---
+
+## 📊 Benchmark Results
+
+### Word Error Rate (WER, %)
+
+| Model | Hindi | Indian English | Code-Switched | Trainable Params |
+|-------|-------|----------------|---------------|------------------|
+| Whisper-Small (zero-shot) | TBD | TBD | TBD | 0 |
+| Whisper-Small (full fine-tune) | TBD | TBD | TBD | 244M |
+| **Laila (LoRA, r=32)** | **TBD** | **TBD** | **TBD** | **3.5M** |
+
+### Character Error Rate (CER, %)
+
+| Model | Hindi | Indian English | Code-Switched |
+|-------|-------|----------------|---------------|
+| Whisper-Small (zero-shot) | TBD | TBD | TBD |
+| Whisper-Small (full fine-tune) | TBD | TBD | TBD |
+| **Laila (LoRA, r=32)** | **TBD** | **TBD** | **TBD** |
+
+### Ablation: LoRA Rank
+
+| Rank | Trainable Params | WER (%) |
+|------|------------------|---------|
+| 8 | TBD | TBD |
+| 16 | TBD | TBD |
+| **32** | **3.5M** | **TBD** |
+| 64 | TBD | TBD |
+
+### Ablation: Adapted Modules & Noise Augmentation
+
+| Adapted Modules | Noise | Hindi | English | Code-Switched |
+|-----------------|-------|-------|---------|---------------|
+| `q_proj`, `v_proj` | Off | TBD | TBD | TBD |
+| `q_proj`, `v_proj` | On | TBD | TBD | TBD |
+| `q_proj`, `k_proj`, `v_proj`, `out_proj` | On | TBD | TBD | TBD |
+| Decoder only | On | TBD | TBD | TBD |
+
+---
+
+## 🎙️ Indian Language Support (22 Adapters Planned)
+
+Laila plans to support all 22 scheduled languages of India:
+
+| Category | Languages |
+|----------|-----------|
+| **Indo-Aryan** | Hindi (हिन्दी), Bengali (বাংলা), Marathi (मराठी), Gujarati (ગુજરાતી), Odia (ଓଡ଼ିଆ), Punjabi (ਪੰਜਾਬੀ), Assamese (অসমীয়া), Urdu (اردو), Konkani (कोंकणी), Nepali (नेपाली), Maithili (मैथिली), Dogri (डोगरी), Sindhi (سنڌي) |
+| **Dravidian** | Tamil (தமிழ்), Telugu (తెలుగు), Kannada (ಕನ್ನಡ), Malayalam (മലയാളം) |
+| **Other** | Sanskrit (संस्कृतम्), Manipuri (মণিপুরী), Bodo (बड़ो), Santali (ᱥᱟᱱᱛᱟᱲᱤ), Kashmiri (کٲشُر) |
+| **Code-Switching** | Hinglish, Tanglish, Benglish, Tinglish, Kanglish, Manglish, Punglish, Marathi-English |
+
+---
+
+## 📁 Dataset v1.0 Statistics
+
+| Split | Modality | Samples | Annotated Labels |
+|-------|----------|---------|------------------|
+| Train | Hindi (Common Voice + AI4Bharat) | TBD | TBD |
+| Val | Hindi | TBD | TBD |
+| Train | Indian-Accented English | TBD | TBD |
+| Val | Indian-Accented English | TBD | TBD |
+| Train | Code-Switched Hindi-English | TBD | TBD |
+| Val | Code-Switched Hindi-English | TBD | TBD |
+| **Total** | **All** | **TBD** | **TBD** |
+
+---
+
+## 🏗️ Technology Stack
+
+| Layer | Technologies |
+|-------|--------------|
+| Base Model | Whisper-Small (244M parameters) |
+| Adaptation | LoRA (PEFT), rank r=32, α=64, dropout=0.05 |
+| Deep Learning | PyTorch 2.0+, HuggingFace Transformers |
+| Audio Processing | torchaudio, librosa, soundfile |
+| Evaluation | jiwer (WER/CER), HuggingFace Evaluate |
+| Training | HuggingFace Trainer, FP16, gradient checkpointing |
+| Inference | PEFT + Transformers, serverless worker architecture |
+| Infrastructure | 1× free-tier T4 GPU (16GB) |
+
+---
+
+## 🔮 What's Next?
+
+- Support for all 22 Indian languages
+- Real-time speech data processing
+- API for companies to use
+- Expansion to other code-switched pairs
+- Deeper integration with AI4Bharat and Bhashini
+- Expansion of noise-robust ASR for defense applications
+
+---
+
+## 📜 License
+
+MIT — Free to use, modify, and distribute.
+
+---
+
+## 🤝 Contributing
+
+PRs welcome! Let's build speech AI for Bharat 🇮🇳