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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