Multi-Language Translator and Speech Recognizer

This project is a multi-language translator and speech recognizer application built with Streamlit. The application allows users to upload audio or video files, transcribe the speech to text, and translate the transcribed text into multiple languages.

Features

• Speech to Text: Transcribes audio from .wav and .mp4 files.
• Multi-Language Translation: Translates text between multiple languages.
• User-Friendly Interface: Built with Streamlit for a simple and interactive user experience.

Models Used

• Speech to Text: The Google Web Speech API is used for converting speech to text. It is known for its accuracy and extensive language support.
• Translation: The Helsinki-NLP/opus-mt-multi model from Hugging Face's Transformers library is used. This model is chosen for its ability to handle multiple language pairs effectively, providing high-quality translations across many languages.

Why Helsinki-NLP/opus-mt-multi?

The Helsinki-NLP/opus-mt-multi model is a robust and versatile translation model that supports a wide range of language pairs. It is built on the MarianMT architecture, which is optimized for efficient translation with high accuracy. This model is particularly suitable for applications requiring translation between less commonly paired languages, making it ideal for this multi-language translator project.

Languages Used in the Project

The project supports translation between the following languages:

1. English (en)
2. French (fr)
3. German (de)
4. Spanish (es)

Speech to Text Technique

We use the Google Web Speech API for the speech to text conversion. This API is chosen due to its high accuracy, ease of use, and support for a wide range of languages.

Video Demonstration

You can view a video demonstration of the project

streamlit-app-2024-06-03-20-06-96.mp4

How to Run This Project

Prerequisites

• Python 3.7+
• git installed on your machine
• ffmpeg installed on your machine

Steps to Set Up and Run the Project

1. Clone the Repository

   git clone <https://github.com/vibhorjoshi/multi_language_app/edit/main>
   cd multi_language_app

2. Create and Activate a Virtual Environment

   On macOS/Linux:

   python3 -m venv venv
   source venv/bin/activate

   On Windows:

   python -m venv venv
   venv\Scripts\activate

3. Install the Dependencies

   pip install -r requirements.txt

4. Install ffmpeg

   On Ubuntu/Debian:

   sudo apt-get update
   sudo apt-get install ffmpeg

   On macOS (with Homebrew):

   brew install ffmpeg

   On Windows:

   • Download ffmpeg from the official website.
   • Follow the instructions to add ffmpeg to your system's PATH.

5. Run the Streamlit App

   streamlit run app.py

Project Structure

multi_language_app/

│ ├── app.py

├── translator.py

├── speech_recog.py

├── requirements.txt

├── venv/ # Virtual environment directory │ └── audio/ # Directory to store audio files for speech recognition

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Deployment

You can access the deployed version of this application here.