bvandeusen
6f84d90dff
Implements full speech-to-speech pipeline (all 4 phases): Backend (Phase 1): - services/stt.py: lazy WhisperModel singleton, run_in_executor transcription - services/tts.py: lazy KPipeline singleton, WAV synthesis at 24kHz/16-bit - routes/voice.py: /api/voice/status, /voices, /transcribe, /synthesise - config.py: VOICE_ENABLED, STT_BACKEND, STT_MODEL, TTS_BACKEND env vars - app.py: load STT/TTS models at startup when VOICE_ENABLED=true - llm.py: voice_mode + voice_speech_style params inject speak-naturally prefix - generation_task.py: voice_mode passed through from chat route - chat.py: "voice" conversation type allowed + excluded from retention cleanup - pyproject.toml + Dockerfile: faster-whisper, kokoro, soundfile dependencies Frontend (Phases 2–4): - composables/useVoiceRecorder.ts: MediaRecorder PTT wrapper - composables/useVoiceAudio.ts: AudioContext WAV playback wrapper - BriefingView.vue: Listen button (TTS read-aloud), auto-TTS mode, mic PTT - VoiceOverlay.vue: global floating PTT button; creates/reuses voice conv; full record→transcribe→stream→TTS flow; Space bar hold-to-talk via App.vue - SettingsView.vue: Voice tab (status badge, speech style, voice/speed) - App.vue: mounts VoiceOverlay; Space keydown/keyup fires voice:ptt-toggle - api/client.ts: getVoiceStatus, getVoiceList, transcribeAudio, synthesiseSpeech Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Fabled Assistant
A self-hosted second brain and project management application with integrated LLM capabilities. Write, organise, and act on your notes and tasks with the help of a local AI assistant — all running on your own hardware.
Features
Notes and tasks with a Markdown editor, sub-tasks, milestones, and kanban project workspaces. AI chat with streaming responses, RAG over your notes, and tool use (web search, calendar, weather). A daily briefing that digests your tasks, RSS feeds, and weather on a schedule. Knowledge graph, per-user/group sharing, PWA with push notifications, an MCP server for external AI clients, and an Android companion app.
Quick Start
Prerequisites: Docker and Docker Compose. 8 GB+ RAM recommended for LLM inference.
Download docker-compose.quickstart.yml from this repo, then:
# Optional but recommended — set a secret key
export SECRET_KEY=your-random-secret-here
docker compose -f docker-compose.quickstart.yml up -d
Open http://localhost:5000. The first user to register becomes admin. Go to Settings → General to pull an LLM model — qwen3:8b or llama3.1:8b are good starting points.
GPU: Ollama runs CPU-only by default. See the comments in
docker-compose.quickstart.ymlto enable NVIDIA GPU passthrough.
Development: To build from source, see Development.
Documentation
| Doc | Contents |
|---|---|
| Architecture | Stack, design decisions, data models, key services |
| Configuration | Environment variables, Docker Compose, production setup, security |
| Features | Detailed feature breakdown and keyboard shortcuts |
| Development | Dev workflow, CI/CD, migrations, release process |
| API Keys & MCP | API key management and Fable MCP install guide |
| SSO / OAuth | OIDC setup for Authentik, Keycloak, and other providers |
| API Reference | All REST API endpoints |
| Android App | Flutter companion app architecture and feature status |
License
This project is privately maintained.