AventIQ-AI/Multilingual_Text_Translator

🌐 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.

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📝 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.

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📊 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.
  

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🧠 Model Details

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

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🔧 Installation

pip install transformers datasets torch gtts

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🚀 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))

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📈 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.

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🏋️ 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

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🔄 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

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📁 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

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⚠️ 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.

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🤝 Contributing

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