---
title: Beta
emoji: 🐢
colorFrom: blue
colorTo: yellow
sdk: static
pinned: true
license: mpl-2.0
short_description: চা খাবা?
---
# শব্দনিক | Shôbdhonic
### **বাংলা NLP-এর নতুন যুগ**
*"ভাষাকে জানো, AI-কে চেনো!"*
*(Unlock Bangla's Future with AI)*
[](https://shobdhonic.com)
[](https://discord.gg/shobdhonic)
[](https://twitter.com/Shobdhonic)
[](https://t.me/Shobdhonic)
[](https://github.com/Shobdhonic)
[](https://huggingface.co/Shobdhonic)
---
## 🚀 **Why Shôbdhonic?**
A **next-gen Bangla NLP platform** built for:
- 🔥 **Gen-Z Creators**: Meme generators, slang translators, TikTok/Reels integrations
- 🏢 **Enterprises**: Sentiment analysis, fraud detection, document processing
- 🇧🇩 **Cultural Preservation**: Digitize literature, dialects, and oral histories
- 🧠 **Research**: Advanced Bangla language models, transformer architectures, and fine-tuning pipelines
- 🌐 **Web3**: Blockchain integration for digital Bangla content authentication
---
## ✨ **Key Features**
| **Category** | **Tools** |
|-----------------------|------------------------------------------------------------------------------------|
| **Gen-Z Playground** | `MemeGPT` • `Slang Translator` • `AI Rap Generator` • `Voice Filters` • `TikTok Content API` |
| **Enterprise NLP** | `Legal Doc Analyzer` • `News Sentiment API` • `Plagiarism Checker` • `Customer Service Bot` • `Bangla Data OCR` |
| **Voice Lab** | `Celebrity Voice Cloning` • `Regional Accent TTS` • `Audio Transcription` • `Dialect Analysis` • `Emotion Detection` |
| **Real-Time AI** | `Trend Predictor` • `Social Media Pulse` • `Ittefaq News Scanner` • `Market Sentiment Analysis` • `Election Opinion Tracker` |
| **Academia** | `Literature Analysis` • `Academic Paper Assistant` • `Educational Content Generator` • `Bangla Research Corpus` |
| **Security Suite** | `Bangla Fraud Detection` • `Phishing Text Analysis` • `Disinformation Tracker` • `Financial Alert System` |
---
## 🎯 **Core Technologies**
### **Models Architecture**
- **ShobdhoBERT**: Transformer-based model trained on 5TB of Bangla text corpus
- **ShobdhoGPT-3.5**: GPT-based generative model fine-tuned on diverse Bangla content
- **DialectDiffusion**: Voice synthesis specialized for regional Bangla dialects
- **BanglaLLM-7B**: Large Language Model optimized for Bangla instruction following
- **Multimodal-Bangla**: Vision-language model for Bangla image-text understanding
### **Data Processing Pipeline**
- Proprietary text normalization for Bangla script variations
- Context-aware slang detection and interpretation
- Real-time news corpus analysis with automated categorization
- Specialized tokenization for Bangla script with compound word handling
- Advanced sentiment analysis for cultural nuances
---
## 🎨 **Brand Identity**
### **Colors**
| Role | Hex | Preview |
|---------------|-----------|------------------------|
| Primary | `#6A5ACD` |  |
| Secondary | `#FF69B4` |  |
| Accent | `#00FFE0` |  |
| Dark Mode | `#1A1A2E` |  |
| Light Mode | `#F5F5F7` |  |
### **Mascot**
**বর্গী বট (Borgi Bot)** – Our street-smart AI mascot for Gen-Z campaigns:
---
## ⚡ **Quick Start**
### **Prerequisites**
- Python 3.10+ / Node.js 18+
- Hugging Face API Key (Register [here](https://huggingface.co/Shobdhonic))
- Docker (optional, for containerized deployment)
- GPU acceleration (recommended for model training/inference)
### **Installation**
```bash
# Clone repo
git clone https://github.com/Shobdhonic/core-engine.git
cd core-engine
# Create virtual environment
python -m venv shobdhonic-env
source shobdhonic-env/bin/activate # On Windows: shobdhonic-env\Scripts\activate
# Install dependencies (Python)
pip install -r requirements.txt
# Or for Node.js
npm install
# Set up environment variables
cp .env.example .env
# Edit .env with your API keys
```
### **Docker Setup**
```bash
# Build the Docker image
docker build -t shobdhonic:latest .
# Run the container
docker run -p 8000:8000 -v $(pwd):/app --env-file .env shobdhonic:latest
```
### **Generate Your First Meme**
```python
from shobdhonic import MemeMaster
# Initialize with your API key
meme_api = MemeMaster(api_key="your_api_key_here")
# Create a meme with custom text and template
meme = meme_api.create(
text="একটা চা আর হয়না? ☕",
template="cha_kaku",
style="viral", # Options: viral, minimal, dramatic, retro
font="bangla_classic",
format="jpg" # Options: jpg, png, gif, mp4
)
# Save the meme
meme.download("output/cha_kaku_meme.jpg")
# Share directly to social media
meme.share(platform="facebook") # Options: facebook, twitter, instagram, whatsapp
```
### **Advanced Voice Cloning**
```python
from shobdhonic import VoiceForge
import numpy as np
# Initialize voice engine
voice_api = VoiceForge(api_key="your_api_key_here")
# Clone a voice with emotion parameters
voice = voice_api.clone(
target_voice="bappa_sir", # Popular Bangla YouTuber
text="ভাই, লাইক আর সাবস্ক্রাইব মনে হয়না!",
emotion="excited", # Options: neutral, sad, excited, angry, persuasive
dialect="dhaka", # Options: dhaka, chittagong, sylhet, rajshahi, khulna, barishal
speed=1.2, # Playback speed multiplier (0.5 - 2.0)
pitch_shift=0.3 # Adjust pitch (-1.0 to 1.0)
)
# Play the generated audio
voice.play()
# Save to file
voice.save("output/bappa_youtube_promo.mp3")
# Get waveform data for further processing
waveform = voice.get_waveform()
frequencies = np.fft.fft(waveform)
```
### **News Sentiment Analysis**
```python
from shobdhonic import NewsAnalyzer
import pandas as pd
import matplotlib.pyplot as plt
# Initialize news analyzer
news_api = NewsAnalyzer(api_key="your_api_key_here")
# Analyze recent articles
results = news_api.analyze(
source="prothom_alo", # Options: prothom_alo, ittefaq, bangla_tribune, bbc_bangla
category="politics", # Options: politics, business, sports, entertainment, tech
date_range="last_7_days", # Options: today, last_24h, last_7_days, last_30_days, custom
sample_size=100 # Number of articles to analyze
)
# Get sentiment breakdown
sentiment_df = pd.DataFrame(results.sentiment_data)
# Plot results
plt.figure(figsize=(10, 6))
plt.bar(sentiment_df['sentiment'], sentiment_df['percentage'])
plt.title('Political News Sentiment Analysis')
plt.xlabel('Sentiment')
plt.ylabel('Percentage (%)')
plt.savefig('output/sentiment_analysis.png')
```
### **Enterprise Document Processing**
```python
from shobdhonic import DocumentProcessor
from shobdhonic.security import SensitiveDataDetector
# Initialize document processor
doc_api = DocumentProcessor(api_key="your_api_key_here")
# Process legal document
processed_doc = doc_api.process(
file_path="contracts/agreement.pdf",
tasks=[
"summarize", # Create executive summary
"extract_entities", # Find people, organizations, dates
"identify_clauses", # Detect important legal clauses
"risk_assessment" # Flag potentially problematic terms
],
output_format="json"
)
# Check for sensitive information
sensitive_detector = SensitiveDataDetector()
security_scan = sensitive_detector.scan(processed_doc.raw_text)
if security_scan.has_sensitive_data:
print(f"WARNING: Found {len(security_scan.findings)} instances of sensitive data")
for finding in security_scan.findings:
print(f"- {finding.type}: {finding.severity} risk level")
# Export processed results
processed_doc.export(
output_path="output/processed_contract.json",
include_metadata=True,
redact_sensitive=True
)
```
---
## 🔋 **Core Modules**
### **Text Processing**
- `shobdhonic.tokenizer`: Advanced Bangla tokenization
- `shobdhonic.transformer`: Pre-trained transformer models
- `shobdhonic.nlp`: Natural language processing utilities
- `shobdhonic.generator`: Text generation capabilities
- `shobdhonic.translator`: Cross-language translation services
### **Audio & Speech**
- `shobdhonic.voice`: Text-to-speech and speech-to-text
- `shobdhonic.audio`: Audio processing utilities
- `shobdhonic.dialect`: Regional dialect processing
### **Media & Content**
- `shobdhonic.meme`: Meme generation engine
- `shobdhonic.social`: Social media integration
- `shobdhonic.content`: Content creation assistants
- `shobdhonic.video`: Video generation and editing
### **Analysis & Intelligence**
- `shobdhonic.sentiment`: Sentiment analysis tools
- `shobdhonic.analytics`: Usage statistics and reporting
- `shobdhonic.trends`: Trend detection and prediction
### **Security & Enterprise**
- `shobdhonic.security`: Security and compliance tools
- `shobdhonic.enterprise`: Enterprise integration utilities
- `shobdhonic.docs`: Document processing pipeline
---
## 📈 **Performance Benchmarks**
| **Task** | **Shôbdhonic** | **Other Bangla NLP** | **Improvement** |
|------------------------------|-----------------|----------------------|-----------------|
| Text Classification | 94.7% | 88.2% | +6.5% |
| Named Entity Recognition | 92.3% | 85.9% | +6.4% |
| Sentiment Analysis | 89.8% | 81.3% | +8.5% |
| Question Answering | 87.6% | 79.1% | +8.5% |
| Text Generation (BLEU) | 0.731 | 0.658 | +11.1% |
| Speech Recognition (WER) | 6.4% | 11.7% | -5.3% (better) |
| Text-to-Speech (MOS) | 4.52/5 | 3.87/5 | +16.8% |
*Benchmarks conducted using standard Bangla test sets and industry metrics. Full methodology available in our [technical paper](https://shobdhonic.com/research/benchmarks).*
---
## 📊 **Enterprise Solutions**
### **Banking & Finance**
- Fraud detection in Bangla SMS/call transcripts
- Customer support automation
- Financial document processing
- Transaction pattern analysis
- Risk assessment NLP
### **Media & Publishing**
- Auto-summarize news articles from Prothom Alo/Ittefaq
- Content recommendation engines
- Automated content tagging
- Engagement prediction
- Toxic comment filtering
### **Education**
- Essay grading and feedback
- Personalized learning content
- Question generation from textbooks
- Academic plagiarism detection
- Educational chatbots in Bangla
### **Government & NGOs**
- Citizen feedback analysis
- Service request categorization
- Policy document processing
- Public sentiment monitoring
- Disinformation detection
---
## 💻 **API Integration**
### **REST API Example**
```javascript
// Using fetch in JavaScript
const fetchMeme = async () => {
const response = await fetch('https://api.shobdhonic.com/v1/create-meme', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
body: JSON.stringify({
text: 'পরীক্ষার রেজাল্ট দেখার পর আমি',
template: 'sad_pepe',
format: 'jpg'
})
});
const data = await response.json();
return data.meme_url;
};
// Call the function
fetchMeme().then(url => {
document.getElementById('meme-image').src = url;
});
```
### **Python SDK Example**
```python
from shobdhonic import ShobdhonicClient
import asyncio
async def main():
# Initialize client
client = ShobdhonicClient(api_key="YOUR_API_KEY")
# Use the sentiment analysis API
result = await client.analyze_sentiment(
text="এই সিনেমাটা দেখে আমি খুবই মুগ্ধ হয়েছি।",
detailed=True
)
print(f"Overall sentiment: {result.sentiment}")
print(f"Confidence score: {result.confidence:.2f}")
print(f"Emotional breakdown: {result.emotions}")
# Use the translation API
translation = await client.translate(
text="আমি বাংলায় কথা বলতে পারি।",
target_language="en"
)
print(f"Translation: {translation.text}")
print(f"Source language detected: {translation.source_language}")
# Run the async function
asyncio.run(main())
```
### **Webhook Integration**
```python
from flask import Flask, request, jsonify
import hmac
import hashlib
app = Flask(__name__)
@app.route('/webhook/shobdhonic', methods=['POST'])
def shobdhonic_webhook():
# Verify the webhook signature
signature = request.headers.get('X-Shobdhonic-Signature')
secret = 'your_webhook_secret'
computed_signature = hmac.new(
secret.encode('utf-8'),
request.data,
hashlib.sha256
).hexdigest()
if not hmac.compare_digest(signature, computed_signature):
return jsonify({'error': 'Invalid signature'}), 401
# Process the webhook data
data = request.json
event_type = data.get('event_type')
if event_type == 'sentiment_alert':
handle_sentiment_alert(data)
elif event_type == 'content_moderation':
handle_content_moderation(data)
elif event_type == 'trend_detected':
handle_trend_detection(data)
return jsonify({'status': 'success'}), 200
def handle_sentiment_alert(data):
# Process sentiment alerts
pass
def handle_content_moderation(data):
# Process content moderation events
pass
def handle_trend_detection(data):
# Process trend detection events
pass
if __name__ == '__main__':
app.run(debug=True, port=5000)
```
---
## 🧩 **Project Structure**
```
shobdhonic/
├── api/ # API endpoints
├── cli/ # Command-line tools
├── core/ # Core functionality
│ ├── models/ # ML models
│ ├── processors/ # Text processors
│ ├── tokenizers/ # Bangla tokenizers
│ └── vectors/ # Word embeddings
├── data/ # Data handling
│ ├── corpus/ # Text corpora
│ ├── loaders/ # Data loaders
│ └── scrapers/ # Web scrapers
├── media/ # Media generation
│ ├── audio/ # Audio processing
│ ├── images/ # Image generation
│ └── video/ # Video processing
├── security/ # Security tools
├── services/ # External services
├── ui/ # User interfaces
│ ├── web/ # Web interface
│ ├── mobile/ # Mobile interface
│ └── widgets/ # Embeddable widgets
├── utils/ # Utility functions
└── tests/ # Test suite
```
---
## 🛠️ **Development Workflow**
### **Setting Up Development Environment**
```bash
# Clone the development repository
git clone https://github.com/Shobdhonic/shobdhonic-dev.git
cd shobdhonic-dev
# Create development environment
python -m venv dev-env
source dev-env/bin/activate
# Install development dependencies
pip install -r requirements-dev.txt
# Set up pre-commit hooks
pre-commit install
```
### **Running Tests**
```bash
# Run all tests
pytest
# Run specific test category
pytest tests/test_tokenizers.py
# Run with coverage report
pytest --cov=shobdhonic --cov-report=html
```
### **Building Documentation**
```bash
# Generate API documentation
cd docs
make html
# View documentation
python -m http.server -d _build/html
```
### **CI/CD Pipeline**
Our continuous integration and deployment pipeline automatically:
1. Runs tests on all pull requests
2. Performs code quality checks
3. Builds and publishes packages on releases
4. Deploys to staging/production environments
5. Updates documentation site
---
## 🤝 **Contribute to Bangla AI**
We welcome contributions from the community! Here's how to get started:
1. **Fork the Repository**: [GitHub/Shobdhonic](https://github.com/Shobdhonic)
2. **Pick an Issue**: Look for issues labeled `good-first-issue`, `help-wanted`, or `Gen-Z feature`
3. **Set Up Your Environment**: Follow the development setup instructions above
4. **Make Your Changes**: Write code and tests for your feature or fix
5. **Submit a Pull Request**: Follow our [Contribution Guidelines](CONTRIBUTING.md)
### **Areas We Need Help With**
- 🧠 **Model Training**: Fine-tuning transformers on Bangla data
- 🎮 **Gen-Z Features**: Cultural memes, slang translators, social integrations
- 📱 **Mobile Development**: React Native components for our SDK
- 🔊 **Voice Data**: Collection and processing of regional dialects
- 📚 **Documentation**: Tutorials, examples, and API documentation
### **Contributor Code of Conduct**
All contributors are expected to adhere to our [Code of Conduct](CODE_OF_CONDUCT.md) which promotes a welcoming, inclusive, and harassment-free experience for everyone.
---
## 📒 **Documentation**
### **API Reference**
Complete API documentation is available at [docs.shobdhonic.com](https://docs.shobdhonic.com)
### **Tutorials**
Step-by-step tutorials for common tasks:
- [Getting Started with Shôbdhonic](https://docs.shobdhonic.com/tutorials/getting-started)
- [Building a Bangla Chatbot](https://docs.shobdhonic.com/tutorials/chatbot)
- [Voice Cloning Basics](https://docs.shobdhonic.com/tutorials/voice-cloning)
- [Meme Generation](https://docs.shobdhonic.com/tutorials/meme-gen)
- [Enterprise Document Processing](https://docs.shobdhonic.com/tutorials/document-processing)
### **Examples**
Explore our [examples directory](https://github.com/Shobdhonic/examples) for complete code samples:
- Basic NLP tasks (tokenization, classification, etc.)
- Voice synthesis and analysis
- Media generation workflows
- Enterprise integration patterns
- Web and mobile application samples
---
## 📜 **License & Ethics**
```text
MIT License | © 2024 Shôbdhonic
*Bangla Data Ethics Pledge:*
- No misuse of dialects/regional languages
- Cite sources like Ittefaq/Prothom Alo
- Free access for academic research and non-profits/NGOs
- Respecting privacy and data sovereignty
- Preserving Bangla linguistic diversity
```
### **Ethical AI Commitment**
At Shôbdhonic, we commit to:
- Transparency in our AI systems
- Fairness and bias mitigation
- Protection of user privacy
- Responsible data collection practices
- Supporting cultural preservation
- Making advanced Bangla NLP accessible to all
Our complete AI Ethics Policy is available [here](https://shobdhonic.com/ethics).
---
## 🧪 **Research**
Our team publishes open research on Bangla NLP:
- [BanglaTransformers: Pre-training Transformers for Bengali NLP](https://arxiv.org/abs/xxxx.xxxxx)
- [Dialect-Aware Speech Synthesis for Low-Resource Languages](https://arxiv.org/abs/xxxx.xxxxx)
- [BanglaEval: Benchmarking NLP Systems for Bengali](https://arxiv.org/abs/xxxx.xxxxx)
Interested in research collaboration? Contact us at research@shobdhonic.com
---
## 🌐 **Connect**
[](https://huggingface.co/Shobdhonic)
[](https://youtube.com/Shobdhonic)
[](https://linkedin.com/company/Shobdhonic)
[](https://medium.com/Shobdhonic)
[](https://discord.gg/shobdhonic)
---
**মহাযুদ্ধ বাংলা ভাষার, আমরা প্রস্তুত!**
*Powered by রক্তে বাংলা, প্রযুক্তিতে Shôbdhonic*