A sophisticated PyQt6-based application that combines speech recognition, multilingual translation, and database management to create a powerful translation tool. The application features real-time audio visualization, bidirectional translation support, and a comprehensive translation history system.
The Voice Translator offers a comprehensive set of features designed to make translation seamless and efficient:
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Speech Recognition and Visualization:
- Real-time audio waveform display during recording
- Silence detection and automatic audio cleanup
- High-accuracy speech-to-text conversion optimized
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Multilingual Translation System:
- Support for bidirectional translation between multiple languages
- Pre-trained neural models for high-quality translations
- Language auto-detection capabilities
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Translation Management:
- Searchable database of all translations
- Advanced filtering and search capabilities
- Intuitive history interface for managing past translations
The project follows a carefully planned modular architecture that separates concerns and promotes code reusability. Each component is designed as an independent module that communicates through well-defined interfaces:
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GUI Module: Handles all user interface components, implementing the Model-View-Controller pattern. The main window and history window are separate classes that inherit from PyQt6 base classes, ensuring clean separation of UI logic.
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Audio Processing Module: Manages all audio-related functionality, from capturing input to processing the signal. It uses dependency injection to remain independent of specific audio processing implementations.
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Translation Module: Encapsulates all translation logic, providing a clean interface to the neural models while hiding the complexity of model management and tokenization.
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Database Module: Handles data persistence with a repository pattern, abstracting the database operations from the rest of the application.
The translation system leverages Hugging Face's transformers library to provide state-of-the-art neural machine translation:
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Model Architecture: Uses the Helsinki-NLP's OPUS-MT models, which are based on the Transformer architecture and trained on millions of parallel sentences.
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Efficient Model Management: Implements lazy loading of models to optimize memory usage. Models are loaded only when needed and cached for subsequent use.
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Pipeline Processing: Translation requests go through a sophisticated pipeline that handles:
- Text normalization and preprocessing
- Tokenization specific to each language pair
- Batch processing for efficient translation
- Post-processing and detokenization
The application implements a robust SQLite3-based storage system with a focus on performance and usability:
The database architecture includes:
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Optimized Schema Design: Carefully designed tables with appropriate indexes for fast querying and efficient storage.
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Connection Management: Implementation of connection pooling and proper transaction handling to ensure data integrity.
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Query Optimization: Prepared statements and optimized queries for fast search and retrieval operations.
The history interface provides:
- Real-time Search: Instant search results as users type, implemented with debouncing for performance.
- Advanced Filtering: Multi-criteria filtering system for source and target languages.
- Efficient Data Display: Virtual scrolling for handling large numbers of translations efficiently.
- Python 3.7+
- PyQt6 and PyQt6-Charts
- SpeechRecognition
- transformers
- torch
- pydub
- sqlite3
- Clone the repository:
git clone https://github.com/Silvano315/Advanced-Voice-Translator-with-Multilingual-Support.git- Navigate to project directory:
cd Advanced-Voice-Translator-with-Multilingual-Support- Install dependencies:
pip install -r requirements.txt- Start the application:
python main_translator.py-
Using the translator:
- Select your desired source and target languages
- Click "Record" and speak clearly (you'll see the audio waveform)
- The recognized text will appear automatically
- Click "Translate" to get your translation
- Save translations you want to keep for later reference
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Managing translations:
- Access your translation history through the history button
- Use the search bar to find specific translations
- Filter by language pairs
- Copy or delete translations as needed
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- Hugging Face for providing the transformer models
- Helsinki-NLP for the multilingual translation models
- PyQt team for the GUI framework
- Google Speech Recognition API for speech-to-text capabilities