Digital Twin

A privacy-focused, fully local system for creating a digital twin trained on your voice, speech patterns, and behavioral data.

Key Principles

• Privacy First - All data stays local, no cloud services
• Reproducible - Open source code and methodology
• Secure - Personal models never published
• Ethical - Speaker diarization to filter out others’ voices

Features

• Voice Cloning - Create a voice synthesis model that sounds like you using Coqui TTS (XTTS v2)
• LLM Personality Model - Fine-tune Llama 3 to capture your communication style and decision-making patterns
• Ambient Audio Collection - Optional wearable hardware for continuous data collection
• Desktop UI - Electron app with chat, recording, training, and plugin management

Desktop UI (Electron)

The Electron UI includes:

• App Shell - Nav rail, top bar, and telemetry footer
• Chat - Polished message layout with bubble sizing
• Record - Unified hero panel with guided steps
• Training - Single guided flow with model selector
• Plugins - Store-style layout with discovery grid

Tech Stack

Audio Processing

• Whisper - Speech-to-text transcription
• pyannote.audio - Speaker diarization
• Coqui TTS (XTTS v2) - Voice cloning and synthesis

LLM

• Llama 3 8B - Base language model
• unsloth/axolotl - LoRA/QLoRA fine-tuning
• Ollama - Local inference

Hardware (Optional)

• Raspberry Pi Zero 2W - Main controller
• I2S MEMS microphone - Audio capture
• Tailscale - Secure networking

Project Roadmap

1. Phase 0 - Manual baseline & pipeline validation
2. Phase 1 - Voice cloning with Coqui TTS
3. Phase 2 - LLM personality model fine-tuning
4. Phase 3 - Integration & interface
5. Phase 4 - Wearable hardware proof-of-concept
6. Phase 5 - Sync & processing infrastructure
7. Phase 6 - Continuous training pipeline

Cost

• Software phases: $0 (using existing hardware)
• No ongoing cloud costs - everything runs locally