Ziggy 🎙️ — 24/7 Voice Assistant

A hands-free, always-on voice companion that runs on your laptop.
Say the wake word, talk naturally, get spoken responses — with conversation
memory, so it remembers what you told it yesterday.

Design principle: silence is free. Wake-word detection runs locally, and
API tokens are consumed only while you're actively talking.

✨ Features

• Wake-word activation — Porcupine-powered on-device "ziggy" detection (push-to-talk fallback included)
• Speech-to-text — local Whisper (tiny), auto-detects Hindi & English, works offline
• Conversational brain — powered by Muse Spark via the Meta Model API
• Text-to-speech — offline voice output, no API key needed
• Conversation memory — every exchange is logged locally; the assistant recalls past context
• Privacy-first — your API key and memory never leave your machine (.env and memory.jsonl are git-ignored)

🏗️ Architecture

 🎤 microphone (24/7, local)
      │
      ▼
 ┌───────────┐   wake word detected   ┌──────────┐
 │  wake.py  │ ─────────────────────▶ │ ears.py  │  audio → text (faster-whisper)
 └───────────┘                        └──────────┘
                                            │ text
                                            ▼
                                      ┌──────────┐
                                      │ brain.py │  Muse Spark (Meta Model API)
                                      └──────────┘
                                            │ text
                                            ▼
 ┌──────────┐                         ┌──────────┐
 │ memory.py│ ◀── every turn logged ── │ mouth.py │  text → speech (pyttsx3)
 └──────────┘                         └──────────┘

Component	Role	Cost
wake.py	Wake-word detection (Porcupine, or push-to-talk)	Free
ears.py	Speech → text (Whisper tiny, CPU)	Free, offline
brain.py	Reasoning & replies (Muse Spark)	~10–15k tokens / hour of active chat
mouth.py	Text → speech	Free, offline
memory.py	JSONL conversation log + recall	Free

🚀 Quick Start