# Claude Voice Commands - Development Roadmap
## Project Overview
Building a voice command interface for Claude Code using MCP (Model Context Protocol) to enable natural language voice interactions.
## Development Sprints
### Sprint 0: Project Setup ⏱️ 30 minutes
**Goal**: Establish project structure and dependencies
**Status**: ✅ Complete
- [x] Create project directory structure
- [x] Initialize git repository
- [x] Create virtual environment
- [x] Install base dependencies (in requirements.txt)
- [x] Set up logging configuration (in .env.example)
- [x] Create initial documentation (README, CLAUDE.md)
**Deliverable**: Working project skeleton with dependencies installed
**Success Metric**: Can run `python -m mcp_server.voice_server --help` without errors
---
### Sprint 1: Basic Voice Capture ⏱️ 1 hour
**Goal**: Prove we can capture and transcribe voice (Updated: ElevenLabs focus)
**Status**: ✅ Complete
- [x] Implement microphone test script with full test suite
- [x] Create ElevenLabs speech recognition integration (primary)
- [x] Add Google Speech Recognition fallback
- [x] Add Whisper local as secondary fallback
- [x] Test audio calibration for ambient noise
- [x] Test ElevenLabs API connection with actual key
- [x] Fix all engine initialization and integration issues
- [ ] Create MCP server with basic voice tools (moved to Sprint 2)
**Deliverable**: Full speech recognition system with ElevenLabs primary + fallbacks
**Success Metric**: ✅ All 3 engines load successfully, microphone tests pass
---
### Sprint 2: MCP Server Foundation ⏱️ 1.5 hours
**Goal**: Create minimal MCP server with basic tools
**Status**: ✅ Complete
- [x] Create `voice_server.py` with FastMCP
- [x] Implement `test_microphone` tool
- [x] Implement `transcribe_once` tool (single command)
- [x] Implement `calibrate_audio`, `list_audio_devices`, `get_engine_status` tools
- [x] Add comprehensive error handling and logging
- [x] Add to Claude Desktop config
- [x] Test MCP server startup and component functionality
**Deliverable**: Professional MCP server with 5 voice tools ready for Claude Code integration
**Success Metric**: ✅ MCP server starts, all components initialize, tools ready for use
---
### Sprint 3: Continuous Listening Mode ⏱️ 2 hours
**Goal**: Enable continuous voice command mode
**Status**: ⏳ Pending
- [ ] Implement threaded listening system
- [ ] Create command queue architecture
- [ ] Add `start_listening` and `stop_listening` tools
- [ ] Implement `get_next_command` tool
- [ ] Handle multiple commands in sequence
**Deliverable**: "Activate voice mode" → continuous listening → "Stop voice mode"
**Success Metric**: Can process 5 commands in sequence
---
### Sprint 4: Wake Word Detection ⏱️ 1.5 hours
**Goal**: Add "Hey Claude" activation
**Status**: ⏳ Pending
- [ ] Implement wake word detection algorithm
- [ ] Create customizable wake word list
- [ ] Add wake word mode vs continuous mode
- [ ] Optimize for low CPU usage
- [ ] Add audio feedback (beep on activation)
**Deliverable**: "Hey Claude, create a function" works naturally
**Success Metric**: Wake word detection > 90% accurate
---
### Sprint 5: Command Intelligence ⏱️ 2 hours
**Goal**: Smart command parsing and context
**Status**: ⏳ Pending
- [ ] Parse commands for intent (create, modify, run, etc.)
- [ ] Handle multi-sentence commands
- [ ] Add command confirmation option
- [ ] Implement command history
- [ ] Create command shortcuts/aliases
**Deliverable**: Natural language commands with context understanding
**Success Metric**: Commands parsed correctly > 95% of time
---
### Sprint 6: Enhanced Recognition ⏱️ 1.5 hours
**Goal**: Improve accuracy and speed
**Status**: ⏳ Pending
- [ ] Add confidence scoring
- [ ] Implement parallel recognition (Google + Whisper)
- [ ] Add noise cancellation preprocessing
- [ ] Cache common phrases for speed
- [ ] Add accent/voice profile support
**Deliverable**: More accurate and faster recognition
**Success Metric**: Recognition time < 1 second
---
### Sprint 7: OpenAI Realtime Integration ⏱️ 2 hours
**Goal**: Add ultra-low latency option (Optional Premium)
**Status**: ⏳ Pending
- [ ] Integrate OpenAI Realtime API
- [ ] Create backend switcher
- [ ] Add streaming recognition
- [ ] Implement cost tracking
- [ ] Add automatic fallback on API errors
**Deliverable**: < 300ms response time option
**Success Metric**: Latency < 300ms when using Realtime API
---
### Sprint 8: User Experience ⏱️ 1.5 hours
**Goal**: Polish the interaction
**Status**: ⏳ Pending
- [ ] Add voice feedback system
- [ ] Create status indicators
- [ ] Implement error recovery
- [ ] Add configuration UI/CLI
- [ ] Create setup wizard
**Deliverable**: Smooth, user-friendly voice interaction
**Success Metric**: Zero crashes in 1 hour of use
---
### Sprint 9: Advanced Features ⏱️ 2 hours
**Goal**: Power user features
**Status**: ⏳ Pending
- [ ] Multi-language support
- [ ] Custom vocabulary training
- [ ] Macro recording (chain commands)
- [ ] Integration with other MCP servers
- [ ] Export/import command history
**Deliverable**: Advanced voice command capabilities
**Success Metric**: Successfully chain 3+ commands
---
### Sprint 10: Documentation & Testing ⏱️ 1 hour
**Goal**: Make it production-ready
**Status**: ⏳ Pending
- [ ] Write comprehensive README
- [ ] Create video demo
- [ ] Add unit tests
- [ ] Performance benchmarking
- [ ] Create troubleshooting guide
**Deliverable**: Production-ready voice command system
**Success Metric**: Another user can set it up in < 10 minutes
---
## Timeline
### Week 1: Foundation
- **Day 1**: Sprint 0 + Sprint 1 (Setup + Basic Voice)
- **Day 2**: Sprint 2 (MCP Server)
- **Day 3**: Sprint 3 (Continuous Listening)
### Week 2: Core Features
- **Day 4**: Sprint 4 (Wake Words)
- **Day 5**: Sprint 5 (Command Intelligence)
- **Day 6**: Sprint 6 (Enhanced Recognition)
### Week 3: Polish
- **Day 7**: Sprint 7 (OpenAI Realtime - optional)
- **Day 8**: Sprint 8 (User Experience)
- **Day 9**: Sprint 9 (Advanced Features)
- **Day 10**: Sprint 10 (Documentation)
---
## Progress Tracking
| Sprint | Status | Started | Completed | Notes |
|--------|--------|---------|-----------|-------|
| 0 | ✅ Complete | 2025-01-03 | 2025-01-03 | Project setup complete |
| 1 | ✅ Complete | 2025-01-03 | 2025-01-03 | ElevenLabs integration complete |
| 2 | ✅ Complete | 2025-01-03 | 2025-01-03 | MCP server with 5 voice tools |
| 3 | ⏳ Pending | - | - | - |
| 4 | ⏳ Pending | - | - | - |
| 5 | ⏳ Pending | - | - | - |
| 6 | ⏳ Pending | - | - | - |
| 7 | ⏳ Pending | - | - | - |
| 8 | ⏳ Pending | - | - | - |
| 9 | ⏳ Pending | - | - | - |
| 10 | ⏳ Pending | - | - | - |
---
## Recognition Backend Options
### Free Options
1. **Google Speech Recognition**
- Pros: Fast (~500ms), good accuracy, no setup
- Cons: Requires internet, rate limits
2. **Whisper Local**
- Pros: Free, offline, good accuracy
- Cons: Higher latency (1-3s), CPU intensive
### Premium Options (Future)
3. **OpenAI Realtime API**
- Pros: Ultra-low latency (<300ms), excellent accuracy
- Cons: ~$0.06/min for audio input
4. **ElevenLabs Voice API**
- Pros: Very low latency, high accuracy
- Cons: Similar pricing to OpenAI
---
## Architecture Decisions
1. **Backend-agnostic design**: Easy to switch between recognition engines
2. **MCP integration**: Seamless Claude Code interaction
3. **Queue-based**: Handle multiple rapid commands
4. **Threaded listening**: Non-blocking continuous operation
5. **Fallback strategy**: Google → Whisper → Error handling
---
## Risk Mitigation
| Risk | Mitigation Strategy |
|------|-------------------|
| High latency | Start with Google, add Realtime API option |
| Poor accuracy | Multiple backend fallback system |
| CPU usage | Wake word detection, optimized threading |
| API costs | Free tier first, optional premium |
| Platform compatibility | Test on macOS/Windows/Linux |
---
## Definition of Done
- [ ] Voice commands work reliably (>95% success rate)
- [ ] Latency acceptable for conversation (<2s free, <300ms premium)
- [ ] Easy setup (<10 minutes for new user)
- [ ] Comprehensive documentation
- [ ] Unit test coverage >80%
- [ ] Works on macOS, Windows, Linux
---
## Notes & Updates
### 2025-01-03
- Project initialized
- Starting with Sprint 0: Project Setup
- Decision to use free backends first (Google + Whisper)
---
*This roadmap is a living document and will be updated as the project progresses.*
