Spaces:
Running
Running
File size: 4,083 Bytes
188a0f1 be04c3d 188a0f1 be04c3d 162f09d 188a0f1 be04c3d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | ---
title: Gu Wiki
emoji: ๐
colorFrom: blue
colorTo: indigo
sdk: static
pinned: false
license: mit
short_description: 'Gujarati Language Model and Speech Recognition (ASR) System '
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/686277c878145a796a2d08f5/a0syUAMIRVb2SCUceB2mK.png
tags:
- track:backyard
- sponsor:nvidia
- achievement:offgrid
- achievement:sharing
- achievement:fieldnotes
---
# GuWiki ๐ฎ๐ณ
**Gujarati Language Model & Automatic Speech Recognition (ASR) System Built from Scratch**
GuWiki is an open-source initiative focused on building AI systems specifically for the Gujarati language. The project combines a custom-trained Gujarati Language Model with a Gujarati Speech Recognition system to make Gujarati knowledge and voice interactions more accessible.
The goal is simple:
> Bring Gujarati knowledge, speech, and AI together in one place.
---
### ๐ง Gujarati Language Model
* Trained from scratch on large-scale Gujarati text data
* Character-level tokenization and encoding
* Knowledge collected from:
* Gujarati Wikipedia
* Public Gujarati text sources
* Curated raw Gujarati documents
* Designed specifically for Gujarati understanding and generation
### ๐๏ธ Gujarati Automatic Speech Recognition (ASR)
* Wav2Vec2-based speech recognition model
* Trained from scratch on Gujarati audio datasets
* Supports Gujarati voice-to-text conversion
* Optimized for real-world Gujarati speech
### ๐ Full Stack Application
* **Frontend:** Next Web Application (`src/`)
* **Backend:** FastAPI (`app/`, `models/`)
* Supports:
* Text-based conversations
* Voice-based conversations
* AI-powered Gujarati knowledge retrieval
---
## ๐ Project Structure
```bash
GuWiki/
โ
โโโ src/ # Frontend Application
โโโ app/ # FastAPI Backend
โโโ models/ # LLM and ASR Models
โโโ requirements.txt
โโโ Dockerfile
โโโ README.md
```
---
## ๐ ๏ธ Tech Stack
### Frontend
* Next
* TypeScript
### Backend
* FastAPI
* Python
### AI & ML
* PyTorch
* Hugging Face Transformers
* Custom Gujarati ASR Model
* Custom Gujarati Character-Level Language Model
---
## ๐ Running Locally
### Clone Repository
```bash
git clone https://github.com/aijadugar/GuWiki.git
cd GuWiki
```
### Backend Setup
```bash
python -m venv venv
source venv/bin/activate
```
Install dependencies:
```bash
pip install -r requirements.txt
```
Run FastAPI server:
```bash
uvicorn app.main:app --reload
```
### Frontend Setup
```bash
cd src
npm install
npm run dev
```
---
## ๐ณ Docker
Build image:
```bash
docker build -t guwiki .
```
Run container:
```bash
docker run -p 8000:8000 guwiki
```
---
## ๐ฏ Vision
Gujarati is one of the most widely spoken languages in India, with over 60 million native speakers worldwide, yet it remains significantly underrepresented in modern AI systems.
GuWiki aims to build:
* High-quality Gujarati language models
* Robust Gujarati speech recognition
* Open-source datasets and benchmarks
* AI tools designed specifically for Gujarati users
---
## ๐ฎ Roadmap
The next generation of GuWiki will include:
### Language Model Improvements
* SentencePiece Tokenization
* Byte Pair Encoding (BPE)
* Larger Gujarati corpora
* Better context understanding
* Improved benchmark performance
### Speech Recognition Improvements
* Larger Gujarati voice datasets
* Noise-robust training
* Better pronunciation handling
* Reduced Word Error Rate (WER)
### Platform Improvements
* Real-time voice conversations
* Knowledge-grounded responses
* Mobile applications
* Community-driven dataset expansion
---
## ๐ค Contributing
Contributions are welcome!
You can help by:
* Improving model architectures
* Adding Gujarati datasets
* Enhancing ASR performance
* Fixing bugs
* Improving documentation
* Creating benchmarks
Fork the repository, create a feature branch, and submit a pull request.
---
## ๐ License
This project is open-source and available under the MIT License.
--- |