Transync

Model Overview

Transync is a multilingual translation model based on the MBart architecture, designed for offline neural machine translation. This repository provides inference capabilities for translating between 50+ languages including all major Indian languages (Hindi, Telugu, Tamil, Bengali, Gujarati, Marathi, Punjabi, Urdu, and more).

Features

• ✅ Multilingual Support: 50+ languages including all major Indian languages
• ✅ High-Quality Translations: Based on the MBart architecture
• ✅ Offline Operation: Works without internet connection
• ✅ Efficient Inference: Optimized for both CPU and GPU
• ✅ Batch Processing: Supports batch translation for efficiency
• ✅ CLI Interface: Command-line interface for easy usage

Supported Languages

The model supports 50+ languages including:

Supported Languages

Short Code	Language	Script	MBart Code
eng	English	Latin	en_XX
hin	Hindi	Devanagari	hi_IN
tel	Telugu	Telugu	te_IN
tam	Tamil	Tamil	ta_IN
mal	Malayalam	Malayalam	ml_IN
kan	Kannada	Kannada	kn_IN
ben	Bengali	Bengali	bn_IN
guj	Gujarati	Gujarati	gu_IN
mar	Marathi	Devanagari	mr_IN
pan	Punjabi	Gurmukhi	pa_IN
urd	Urdu	Arabic	ur_PK
asm	Assamese	Bengali	as_IN
npi	Nepali	Devanagari	ne_NP
ory	Odia	Odia	or_IN
san	Sanskrit	Devanagari	sa_IN
mai	Maithili	Devanagari	mai_IN
brx	Bodo	Devanagari	brx_IN
doi	Dogri	Devanagari	doi_IN
gom	Konkani	Devanagari	gom_IN
mni	Meitei	Bengali	mni_IN
sat	Santali	Ol Chiki	sat_IN
kas	Kashmiri	Arabic	ks_IN
snd	Sindhi	Arabic	sd_IN

Installation

Install the required dependencies:

pip install -r requirements.txt

CLI Example

# Translate single text
python transync_inference.py eng hin "Hello, how are you?"

# Batch translation from file
python transync_inference.py --batch eng hin -f input.txt -o output.txt

Repository Structure

transync/
├── config.json                 # Model configuration
├── generation_config.json      # Generation configuration
├── pytorch_model.bin           # Model weights
├── sentencepiece.bpe.model     # SentencePiece tokenizer model
├── tokenizer_config.json       # Tokenizer configuration
├── special_tokens_map.json     # Special tokens mapping
├── README.md                   # This file
├── LICENSE                     # MIT License
├── requirements.txt            # Dependencies
└── transync_inference.py       # Inference code

Model Architecture

This model is built on the MBart (Multilingual BART) architecture, which is a sequence-to-sequence model pre-trained on multilingual data. It leverages the power of BART's bidirectional encoder and autoregressive decoder for translation tasks.

Tokenizer

The model uses SentencePiece tokenizer for subword tokenization. The tokenizer is compatible with the MBart50 tokenizer format and supports 50+ languages.

Limitations

• Translation quality varies across language pairs.
• The model should be evaluated before production use.
• Performance depends on hardware and input length.

License

MIT