sl-mechanical
fabfd5e974
Implement context-aware text-to-speech with emotion-driven expression for SaltyBot. Features: ✓ Context-aware greetings (time of day, person names, emotion) ✓ Priority queue management (safety > social > idle) ✓ Emotion-based rate/pitch modulation (happy: faster+higher, sad: slower+lower) ✓ Integration with emotion engine (Issue #429) and TTS service (Issue #421) ✓ Configurable personality parameters ✓ Person recognition for personalized responses ✓ Queue management with 16-item buffer Architecture: Node: tts_personality_node - Subscribes: /saltybot/tts_request, /saltybot/emotion_state, /saltybot/person_detected - Publishes: /saltybot/tts_command (formatted for TTS service), /saltybot/personality_state - Runs worker thread for asynchronous queue processing Personality Parameters: - Name: "Luna" (default, configurable) - Speed modulation: happy=1.1x, sad=0.9x, neutral=1.0x - Pitch modulation: happy=1.15x, sad=0.85x, neutral=1.0x - Time-based greetings for 4 periods (morning, afternoon, evening, night) - Known people mapping for personalization Queue Priority Levels: - SAFETY (3): Emergency/safety messages - SOCIAL (2): Greetings and interactions - IDLE (1): Commentary and chatter - NORMAL (0): Default messages Files Created: - saltybot_tts_personality package with main personality node - config/tts_personality_params.yaml with configurable parameters - launch/tts_personality.launch.py for easy startup - Unit tests for personality context and emotion handling - Comprehensive README with usage examples Integration Points: - Emotion engine (Issue #429): Listens to emotion updates - TTS service (Issue #421): Publishes formatted commands - Jabra SPEAK 810: Output audio device - Person tracking: Uses detected person names Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>