Multilingual Language Translation using LSTM

This project implements a Language Translation system using Long Short-Term Memory (LSTM) networks. The model is trained to perform translation between English and multiple languages, including Tamil, French, and Spanish.

Technologies Used

• LSTM Model: The core of the translation is based on the LSTM (Long Short-Term Memory) neural network architecture, providing a solid foundation for sequence-to-sequence learning.

• Streamlit: The application leverages Streamlit, a user-friendly Python library for creating web applications with minimal effort.

How to Use

1. Clone the repository:

   git clone https://github.com/ramakrishnan2503/Language_Translation_using_LSTM.git

2. Run the app:

   streamlit run app.py

3. Open your web browser and navigate to the provided URL.

4. Enter an English sentence in the input box, select the desired translation language, and click the "Translate" button.

Model Details

The translation model utilizes LSTM (Long Short-Term Memory) networks for sequence-to-sequence learning, enabling effective language translation.

• LSTM Nodes: 256
• Embedding Size: 100
• Batch Size: 64
• Epochs: 20

Supported Languages

• English to Tamil
• English to French
• English to Spanish
• Tamil to English
• French to English
• Spanish to English

File Structure

• app.py: Main script for the Streamlit web application.
• eng_tam_model.py: Script for loading the trained LSTM model and performing translations for English to Tamil.
• tam_eng_model.py: Script for loading the trained LSTM model and performing translations for Tamil to English.
• eng_fre_model.py: Script for loading the trained LSTM model and performing translations for English to French.
• fre_eng_model.py: Script for loading the trained LSTM model and performing translations for French to English.
• eng_spa_model.py: Script for loading the trained LSTM model and performing translations for English to Spanish.
• spa_eng_model.py: Script for loading the trained LSTM model and performing translations for Spanish to English.

Acknowledgments

The LSTM model is trained for basic translation purposes and is not fine-tuned on vast datasets.

Download Model,Encoder,Decoder and Tokenizer:Drive

Note: The models and tokenizers have been saved for convenient usage.