Voice Interview Assistant - Production Ready

A professional voice recording and AI-powered interview assistance application designed for software development interview preparation.

🚀 Features

• High-quality voice recording with configurable audio settings
• Real-time speech transcription using OpenAI Whisper
• AI-powered interview responses via Ollama or compatible APIs
• Production-ready architecture with proper error handling and logging
• Configurable settings via INI file
• Professional UI with dark theme and responsive design
• Thread-safe operations with proper resource management
• Comprehensive logging for debugging and monitoring

📋 Requirements

System Requirements

• Python 3.8 or higher
• Audio input device (microphone)
• 4GB+ RAM (8GB recommended for larger Whisper models)
• GPU with CUDA support (optional, for faster transcription)

Software Dependencies

• Ollama or compatible AI service running locally
• PortAudio (for audio recording)

🛠️ Installation

1. Clone or Download

# Save the main application file as voice_assistant.py

2. Install Python Dependencies

# Install required packages
pip install -r requirements.txt

# Optional: Install CUDA support for faster transcription
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118

3. Install System Dependencies

Windows

# PortAudio is usually included with sounddevice
# If you encounter issues, install Visual C++ Build Tools

macOS

# Install using Homebrew
brew install portaudio

Linux (Ubuntu/Debian)

sudo apt-get update
sudo apt-get install portaudio19-dev python3-pyaudio

4. Setup AI Service (Ollama)

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull the required model
ollama pull llama3

# Start Ollama service
ollama serve

⚙️ Configuration

The application uses a config.ini file for configuration. On first run, it will create a default configuration file that you can customize.

Key Configuration Options

Audio Settings

• sample_rate: Audio sample rate (default: 44100)
• channels: Number of audio channels (default: 1)
• dtype: Audio data type (default: int16)

Whisper Model

• model: Whisper model size (tiny, base, small, medium, large)
• device: Processing device (auto, cpu, cuda)

AI Service

• api_url: AI service endpoint
• model: AI model name
• timeout: Request timeout in seconds

Logging

• level: Log level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
• file: Log file path (optional)

🚀 Usage

Starting the Application

python voice_assistant.py

Basic Operation

1. Start Recording: Press SPACE or click the microphone button
2. Stop Recording: Press SPACE again or click the stop button
3. View Results: The popup window shows transcription and AI response
4. Toggle Popup: Double-click the microphone button

Keyboard Shortcuts

• SPACE: Start/Stop recording
• Double-click mic button: Toggle popup window

🏗️ Production Deployment

1. Environment Setup

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/macOS
# or
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

2. Configuration Management

# Copy and customize configuration
cp config.ini.template config.ini
# Edit config.ini with your settings

3. Service Setup (Linux)

Create a systemd service file:

[Unit]
Description=Voice Interview Assistant
After=network.target

[Service]
Type=simple
User=your_username
WorkingDirectory=/path/to/voice-assistant
ExecStart=/path/to/venv/bin/python voice_assistant.py
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target

4. Monitoring and Logging

• Check logs in the configured log file
• Monitor system resources (CPU, memory, GPU)
• Set up log rotation for production environments

🔧 Troubleshooting

Common Issues

Audio Recording Problems

# Check available audio devices
python -c "import sounddevice as sd; print(sd.query_devices())"

# Test microphone
python -c "import sounddevice as sd; import numpy as np; print('Recording...'); data = sd.rec(44100, samplerate=44100, channels=1); sd.wait(); print('Done')"

Whisper Model Loading Issues

• Ensure sufficient RAM is available
• Try a smaller model (tiny, base) if memory is limited
• Check CUDA installation for GPU acceleration

AI Service Connection Issues

• Verify Ollama is running: curl http://localhost:11434/api/tags
• Check firewall settings
• Verify model availability: ollama list

UI Issues

• Ensure tkinter is installed (usually included with Python)
• Check display settings for popup window positioning
• Verify window manager compatibility

Performance Optimization

For Better Transcription Speed

• Use GPU acceleration (CUDA)
• Choose appropriate Whisper model size
• Optimize audio settings

For Lower Memory Usage

• Use smaller Whisper models (tiny, base)
• Reduce audio buffer sizes
• Close popup when not needed

📊 Architecture Overview

Key Components

1. ConfigManager: Handles application configuration
2. AudioManager: Manages audio recording with thread safety
3. AIService: Handles Whisper transcription and AI API calls
4. VoiceInterviewAssistant: Main application controller
5. UI Components: Professional GUI with responsive design

Thread Safety

• All audio operations are thread-safe
• Proper resource cleanup on shutdown
• Graceful handling of interruptions

Error Handling

• Comprehensive exception handling
• Graceful degradation on errors
• User-friendly error messages
• Detailed logging for debugging

🔒 Security Considerations

• Audio data is processed locally (privacy-first)
• Temporary files are cleaned up automatically
• No sensitive data is stored permanently
• API calls use session management

📈 Monitoring and Maintenance

Health Checks

• Monitor log files for errors
• Check AI service availability
• Verify audio device connectivity
• Monitor system resources

Updates and Maintenance

• Regularly update dependencies
• Monitor Whisper model updates
• Check Ollama service updates
• Review and rotate log files

🤝 Contributing

When contributing to the production version:

1. Follow Python PEP 8 style guidelines
2. Add comprehensive error handling
3. Include logging for debugging
4. Write unit tests for new features
5. Update configuration documentation
6. Test on multiple platforms

📄 License

This production-ready version includes enterprise-grade features and should be used according to your organization's software licensing policies.

📞 Support

For production deployment support:

• Check logs for detailed error information
• Verify all dependencies are correctly installed
• Test individual components (audio, transcription, AI service)
• Monitor system resources during operation

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Production Notes: This version includes comprehensive error handling, logging, configuration management, and thread safety suitable for production environments. Always test thoroughly in your specific environment before deployment.