🌐 T5-Based Multilingual Text Translator

This repository presents a fine-tuned T5-small model for multilingual text translation across English, French, German, Italian, and Portuguese. It includes quantization for efficient inference and speech synthesis support for accessibility.

📝 Problem Statement

The goal is to translate text between English and multiple European languages using a transformer-based model. Instead of using black-box APIs, this project fine-tunes the T5 model on parallel multilingual corpora, enabling offline translation and potential customization.

📊 Dataset

Source: Custom parallel corpus (.txt files) with one-to-one sentence alignments.
Languages Supported:
- English
- French
- German
- Italian
- Portuguese
Structure:
- Each language has a corresponding .txt file.
- Lines are aligned by index to form translation pairs.

Example Input Format:

Source: translate English to French: I am a student.
Target: Je suis un étudiant.

🧠 Model Details

Architecture: T5-small
Tokenizer: T5Tokenizer
Model: T5ForConditionalGeneration
Task Type: Sequence-to-Sequence Translation (Supervised Fine-tuning)

🔧 Installation

pip install transformers datasets torch gtts

🚀 Loading the Model

from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch

# Load quantized model (float16)
model = T5ForConditionalGeneration.from_pretrained("quantized_model", torch_dtype=torch.float16)
tokenizer = T5Tokenizer.from_pretrained("quantized_model")

# Translation example
source = "translate English to German: How are you?"
inputs = tokenizer(source, return_tensors="pt", padding=True, truncation=True)

with torch.no_grad():
    outputs = model.generate(**inputs)

print("Translated:", tokenizer.decode(outputs[0], skip_special_tokens=True))

📈 Performance Metrics

As this project is based on a single-epoch fine-tuning, performance metrics are not explicitly computed. For a production-level system, BLEU or ROUGE scores should be evaluated.

🏋️ Fine-Tuning Details

📚 Dataset Preparation

A total of 5 text files (english.txt, french.txt, etc.)
Each sentence aligned by index for parallel translation.

🔧 Training Configuration

Epochs: 1
Batch size: 4
Max sequence length: 128
Model base: t5-small
Framework: Hugging Face Transformers + PyTorch
Evaluation strategy: 10% test split

🔄 Quantization

Post-training quantization was performed using .half() precision (FP16) to reduce model size and improve inference speed.

# Load full-precision model
model_fp32 = T5ForConditionalGeneration.from_pretrained("model")

# Convert to half precision
model_fp16 = model_fp32.half()
model_fp16.save_pretrained("quantized_model")

Model Size Comparison:

Type	Size (KB)
FP32 (Original)	~6,904 KB
FP16 (Quantized)	~3,452 KB

📁 Repository Structure

.
├── model/                       # Contains FP32 model files
│   ├── config.json
│   ├── model.safetensors
│   ├── tokenizer_config.json
│   └── ...
├── quantized_model/            # Contains FP16 quantized model files
│   ├── config.json
│   ├── model.safetensors
│   ├── tokenizer_config.json
│   └── ...
├── README.md                   # Documentation
└── multilingual_translator.py  # Training and inference script

⚠️ Limitations

Trained on a small dataset with only one epoch — may not generalize well to all phrases or complex sentences.
Language coverage is limited to 5 predefined languages.
gTTS is dependent on Google API and requires internet access.

🤝 Contributing

Feel free to submit issues or PRs to add more language pairs, extend training, or integrate UI for real-time use.