EchoTranslate: Machine Translation and Speech Detection

A powerful project for transforming spoken language into structured text, combining cutting-edge speech recognition and machine translation. Ideal for applications needing high accuracy and efficiency in language processing.

🎯 Project Goals

• 🔍 Accurate Speech Detection: Recognize spoken language and convert it into text.
• 🌐 Efficient Machine Translation: Translate detected text into the target language accurately.
• 🛠️ Data Handling and Preprocessing: Clean and structure data for reliable model performance.
• 📊 Evaluation and Metrics: Measure accuracy and effectiveness using industry-standard metrics.

📑 Table of Contents

1. Features
2. Getting Started
3. Usage
4. Project Structure
5. Contributing
6. License

🚀 Features

• 🎙️ End-to-End Speech Recognition and Translation: From audio input to translated text output.
• ⚙️ Customizable Configurations: Adaptable model settings for various applications.
• 📈 Evaluation Metrics: BLEU score and Word Error Rate (WER) for translation quality assessment.
• 🔧 Data Preprocessing Pipeline: Ensures clean data for robust model results.

🛠️ Getting Started

📋 Prerequisites

To run this project, you’ll need:

• Python 3.x
• Jupyter Notebook
• Required libraries in requirements.txt:
  • torch - for deep learning
  • transformers - for language models
  • librosa - for audio processing
  • speechrecognition - for speech-to-text
  • scipy - for signal processing

💻 Installation

1. Clone the repository:

   git clone https://github.com/yourusername/machine_translation_speech_detection.git
   cd machine_translation_speech_detection