FlashGinie

🎧 VoiceMoodMirror: Real-Time Voice Emotion Analyzer & Feedback

📌 Project Overview

VoiceMoodMirror captures a user's speech (a few sentences), analyzes prosodic features like pitch, tempo, energy, and spectral characteristics to infer their current mood or emotional state, then provides visual feedback (e.g., color/animation dashboard) and/or suggests or plays music that matches or modulates that mood.

💡 Use Cases

• Self-awareness / mood journaling tools
• Wellness apps (e.g., calming music when stressed)
• Interactive installations or smart mirrors
• Accessibility/emotion-aware assistants

🚀 Quick Start

Installation

# Clone the repository
git clone <repository-url>
cd voicemoodmirror

# Install dependencies
pip install -r requirements.txt

Running the Application

# Run the Streamlit dashboard
streamlit run ui/dashboard.py

# Or run the demo notebook
jupyter notebook examples/demo_notebook.ipynb

Running Tests

# Run all tests
pytest

# Run tests with coverage
pytest --cov=.

# Run specific test file
pytest tests/test_audio_recorder.py

🧠 Core Components

1. Audio Capture & Feature Extraction

• audio/recorder.py: Microphone capture, buffering, preprocessing
• audio/feature_extractor.py: Extract pitch, tempo, energy, MFCCs, etc. (librosa)

2. Emotion Analysis

• emotion/prosody_classifier.py: Rule-based or ML model mapping prosodic features to mood
• emotion/mood_mapper.py: Maps inferred mood to visuals and music tags

3. Music Selection

• music/music_selector.py: Selects/queues music based on mood (local library or public API)
• music/playlist_builder.py: Builds adaptive playlists (e.g., calming if stressed)

4. User Interface

• ui/dashboard.py: Visual feedback (e.g., real-time mood meter, color gradients)

5. Utilities

• utils/smoothing.py: Temporal smoothing of noisy mood predictions

🔧 Features

• 🎤 Live or recorded voice input
• 📊 Real-time visualization of emotional state
• 🎶 Adaptive music recommendation/player
• 🔁 Temporal smoothing to avoid jittery mood flicker
• ⚙️ Configurable mood mappings (user can choose whether to reflect mood or counteract it)
• 🧠 Optional "mood history" log for self-reflection

🧪 Possible Enhancements

• Add speech-to-text and combine semantic sentiment with prosody for richer inference
• Personalized calibration per user (baseline pitch/tempo)
• Support for multilingual voice input
• Mobile/web version using Web Audio API and TensorFlow.js
• Emotion change detection and notifications (e.g., "You seem more stressed than 10 minutes ago")
• Integrate with smart home to adjust lighting/music based on mood

📁 Project Structure

voicemoodmirror/
│
├── audio/
│   ├── recorder.py              # Microphone capture, buffering, preprocessing
│   └── feature_extractor.py     # Extract pitch, tempo, energy, MFCCs, etc. (librosa)
│
├── emotion/
│   ├── prosody_classifier.py    # Rule-based or ML model mapping prosodic features to mood
│   ├── model_training.py        # (Optional) Train a lightweight model on synthetic / annotated data
│   └── mood_mapper.py           # Maps inferred mood to visuals and music tags
│
├── music/
│   ├── music_selector.py        # Selects/queues music based on mood (local library or public API)
│   └── playlist_builder.py      # Builds adaptive playlists (e.g., calming if stressed)
│
├── ui/
│   └── dashboard.py             # Visual feedback (e.g., real-time mood meter, color gradients)
│
├── utils/
│   └── smoothing.py             # Temporal smoothing of noisy mood predictions
│
├── tests/
│   ├── test_audio_recorder.py
│   ├── test_feature_extractor.py
│   ├── test_prosody_classifier.py
│   ├── test_mood_mapper.py
│   ├── test_music_selector.py
│   └── test_smoothing.py
│
├── examples/
│   └── demo_notebook.ipynb      # Interactive demo with recorded audio samples
│
├── requirements.txt
└── README.md

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

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