mirror of
https://github.com/Alkatrazz24/Funk-lab.git
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feat(stt): assistant vocal Jarvis — client pipx + STT-server in-cluster (#4)
* feat(stt): cadrage + squelette assistant vocal Jarvis Conception validée du projet STT — assistant vocal/HUD du homelab Funk : - HUD web sur-mesure + STT/TTS local (faster-whisper + Piper) - Packaging commande pipx (stt), démarrage auto systemd --user - Cerveau 3 modes + auto-détection LAN : hermes / local-direct / claude-direct - Mémoire 3 tiers : SQLite local + Qdrant s01 + GitHub (distillée, versionnée) Réutilise tools/hermes-voice, LiteLLM, Hermes Agent. Squelette + doc admin/ia/stt.md, implémentation par phases (roadmap dans le doc). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013FmcxGsyXZXogiAHQLjnZT * feat(stt): phase 1-2 — commande, backend vocal, routeur cerveau, HUD MVP - cli.py : commande `stt` (--setup, --mode, --no-tts) - config.py : défauts embarqués + ~/.config/stt/stt.toml - voice/engine.py : refactor de hermes-voice en classe avec callbacks d'état - brain/router.py : 3 modes (hermes SSH / local LiteLLM / claude API) + auto-détection LAN - server/app.py : HTTP statique (HUD) + websocket (états → HUD) - memory/store.py : tier local SQLite (Qdrant + sync GitHub = phase 4) - hud/index.html : HUD MVP (visualiseur d'état + conversation) Vérifié hors-LAN : py_compile, --help, config, routeur (→ claude), mémoire SQLite. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013FmcxGsyXZXogiAHQLjnZT * fix(stt): embarquer le HUD dans le package (404 après pipx install) Le HUD était à la racine du projet (stt/hud/) donc absent du package installé par pipx → HTTP 404 sur /. Déplacé dans le package (stt/stt/hud/) + package-data, HUD_DIR ajusté. Vérifié : le wheel contient bien stt/hud/index.html. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013FmcxGsyXZXogiAHQLjnZT * refactor(stt): pivot client-serveur — STT-server in-cluster + client pipx Sépare STT en deux : - stt/client/ : commande `stt` (pipx), voix locale (Whisper/Piper) + HUD ; envoie le texte au serveur via api.py (ServerClient → POST /v1/ask). URL serveur paramétrable, pas de cerveau local (suppression du routeur 3 modes). - stt/server/ : STT-server FastAPI (conteneur), /healthz + /v1/ask → LiteLLM (Qwen3/Claude). Déploiement cluster : - k8s/apps/stt/ : Deployment, Service, IngressRoute (stt.lab.local), litellm-ext (Service + Endpoints → 192.168.10.1:4000 pour joindre LiteLLM hors cluster) - k8s/apps-of-apps/apps/stt.yaml : Application ArgoCD (depuis main) - .github/workflows/build-stt-server.yml : build/push image → ghcr.io/alkatrazz24/funk-stt-server Inférence/chat seulement (outils Hermes 'agir sur Funk' = phase ultérieure, API :8080 à spécifier). Vérifié : py_compile client+serveur, YAML manifests, ServerClient. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013FmcxGsyXZXogiAHQLjnZT --------- Co-authored-by: Claude <noreply@anthropic.com>
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46
.github/workflows/build-stt-server.yml
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.github/workflows/build-stt-server.yml
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name: build-stt-server
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# Construit et pousse l'image du STT-server vers ghcr.io quand stt/server/ change.
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on:
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push:
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branches: ["main", "claude/**"]
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paths:
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- "stt/server/**"
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- ".github/workflows/build-stt-server.yml"
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workflow_dispatch:
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env:
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IMAGE: ghcr.io/alkatrazz24/funk-stt-server
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jobs:
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build:
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runs-on: ubuntu-latest
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permissions:
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contents: read
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packages: write
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steps:
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- uses: actions/checkout@v4
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- name: Log in to GHCR
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uses: docker/login-action@v3
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with:
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registry: ghcr.io
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Metadata (tags)
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id: meta
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uses: docker/metadata-action@v5
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with:
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images: ${{ env.IMAGE }}
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tags: |
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type=raw,value=latest,enable={{is_default_branch}}
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type=sha,format=short
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- name: Build & push
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uses: docker/build-push-action@v6
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with:
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context: stt/server
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push: true
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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admin/ia/stt.md
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admin/ia/stt.md
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# STT — Assistant vocal "Jarvis" du homelab Funk
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**STT** est l'interface vocale et graphique de Funk : un assistant type *Jarvis / Iron Man*
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qui écoute, parle et affiche un HUD animé. **Architecture client-serveur** : un client `stt`
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sur le poste (voix + HUD) qui interroge un **STT-server in-cluster** (orchestration AI).
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Objectif ultérieur : « agir sur le homelab » via les outils de Hermes (phase 7).
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> **Nom de code** : `STT`. À l'origine « Speech-To-Text », mais le projet couvre toute la
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> chaîne voix → cerveau → voix + interface + mémoire. Renommable plus tard sans impact
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> technique (FRIDAY, etc.) — c'est juste un identifiant de répertoire/commande.
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> ⚠️ **2026-06-17** — client (phase 1) + serveur (phase 2) + manifests cluster (phase 3) écrits.
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> Reste : build image (CI), déploiement ArgoCD (après merge sur `main`), test audio sur poste.
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---
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## Principe directeur : réutiliser, ne pas réinventer
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~70 % du backend existe déjà dans Funk. STT n'ajoute que **le visage** (HUD), la
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**personnalisation**, la **mémoire multi-tiers** et le **packaging en commande**.
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| Besoin Jarvis | Brique Funk réutilisée | État |
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|---|---|---|
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| Modèle local **ou** Claude | LiteLLM `:4000` + `hermes-default` + `hermes-switch qwen\|claude` | ✅ opérationnel |
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| Agir sur le homelab | Hermes Agent (`:8080`, profils funk-ai/system/monitor/brain) | ✅ opérationnel |
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| Voix (STT + TTS + wake word) | `tools/hermes-voice/` — faster-whisper + Piper + webrtcvad | ✅ existe (CLI only) |
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| Démarrage auto au boot | pattern `systemd --user` (`tools/hermes-voice/install-service.sh`) | ✅ éprouvé |
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| Mémoire sémantique | Qdrant `:6333` + RAG (`rag-query`/`rag-ingest`) sur s01 | ⚠️ **HS depuis 05/06** |
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Maillon réellement manquant : **l'interface graphique HUD** + le packaging + la mémoire perso.
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---
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## Décisions d'architecture (verrouillées 2026-06-17)
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| Décision | Choix retenu |
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|---|---|
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| Architecture | **Client-serveur** : client sur le poste, STT-server **in-cluster** (révise le tout-local) |
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| Interface | **HUD web sur-mesure** côté client (canvas/WebGL) |
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| STT / TTS | **Local sur le poste** (faster-whisper CPU + Piper) — le serveur ne touche pas à l'audio |
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| Packaging | Client : **commande `stt` via pipx** (`#subdirectory=stt/client`). Serveur : **conteneur** (ghcr) déployé par ArgoCD |
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| Cerveau | **Côté serveur** : route vers LiteLLM `:4000` (Qwen3 / Claude). Outils Hermes = phase ultérieure |
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| Client → serveur | Client **serveur-only** (pas de repli Claude). URL serveur **paramétrable** |
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| Mémoire | **Côté serveur** (futur) : Qdrant s01 + distillée GitHub. Client : cache local SQLite |
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> Pivot 2026-06-17 (post-test) : on est passé du tout-local à un modèle client-serveur.
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> Le « cerveau » (ex-routeur 3 modes côté client) a migré côté serveur.
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---
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## Architecture
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```
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LAN / *.lab.local
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┌─ POSTE — client `stt` (pipx) ─┐ ┌─ CLUSTER k8s (namespace ai) ─────────┐
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│ • micro + VAD + wake word │ HTTP │ STT-server (Deployment + Service) │
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│ • faster-whisper (STT) │ ─────▶ │ GET /healthz │
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│ • Piper (TTS) │ POST │ POST /v1/ask {text} → {reply} │
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│ • HUD web (ui/ + hud/) │ /v1/ask│ brain → LiteLLM (httpx) │
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│ • api.py → ServerClient │ ◀───── │ IngressRoute : stt.lab.local │
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└────────────────────────────────┘ reply │ Service Endpoints : litellm-ext │
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└──────────────┬─────────────────────────┘
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▼
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LiteLLM :4000 (storage-01, hors cluster)
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→ Qwen3 (g01) / Claude (hermes-default)
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```
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### Le cerveau — côté serveur
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Le STT-server appelle **LiteLLM `:4000`** (OpenAI-compatible), joint depuis le cluster via
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un Service sans sélecteur + Endpoints manuel (`litellm-ext` → `192.168.10.1:4000`). LiteLLM
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route lui-même vers Qwen3 (g01) ou Claude selon l'alias `hermes-default` / `hermes-switch`.
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> **« Agir sur Funk »** (outils de l'agent Hermes via le gateway `:8080`) n'est **pas** dans le
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> MVP : l'API `:8080` n'est pas spécifiée, et depuis un pod le SSH vers Hermes est impraticable.
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> MVP = inférence/chat. L'intégration Hermes est une étape ultérieure.
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### La mémoire — côté serveur (futur)
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- **Client** : cache local SQLite (`~/.local/share/stt/`, gitignoré) — historique brut.
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- **Serveur** (à venir) : Qdrant s01 (sémantique, partagé RAG) + mémoire **distillée**
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versionnée dans `stt/server/memory/distilled/`. Sessions multi-clients.
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> ⚠️ **Vie privée** : seule la mémoire distillée serait committée. Repo privé impératif.
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---
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## Composants
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| Composant | Emplacement | Description |
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|---|---|---|
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| Client `stt` | `stt/client/` (pipx) | `stt`, `stt --setup`, `stt --server <url>`, `stt --no-tts` |
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| — voix | `stt/client/stt/voice/` | wake word, STT faster-whisper, TTS Piper |
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| — api | `stt/client/stt/api.py` | `ServerClient` → `POST /v1/ask` |
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| — UI/HUD | `stt/client/stt/ui/` + `hud/` | HTTP statique + websocket d'états ; HUD embarqué dans le package |
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| STT-server | `stt/server/` (conteneur) | FastAPI : `/healthz`, `/v1/ask` ; `brain.py` → LiteLLM |
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| Image | `ghcr.io/alkatrazz24/funk-stt-server` | construite par `.github/workflows/build-stt-server.yml` |
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| Manifests | `k8s/apps/stt/` | Deployment, Service, IngressRoute (`stt.lab.local`), `litellm-ext` |
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| Application ArgoCD | `k8s/apps-of-apps/apps/stt.yaml` | déploie depuis `main` |
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---
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## Personnalisation (« l'image, le design, la voix »)
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Côté client, pilotable depuis `stt/client/config/` + l'écran de réglages du HUD :
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- **Avatar / image** (`config/avatars/`), **thème** (`config/themes/`),
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**voix** Piper `.onnx` (`config/voices/`), **wake word**, **URL serveur**.
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---
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## Prérequis / dépendances
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- **Poste** : micro, Python 3.11+, pipx, navigateur. Piper + voix dans `~/.local/share/piper/`, `aplay`.
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- **Cluster** : image poussée sur ghcr ; ArgoCD déploie **depuis `main`** (donc merge requis) ;
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ghcr privé ⇒ éventuel `imagePullSecret`.
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- **LiteLLM** joignable depuis le cluster (`192.168.10.1:4000`).
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---
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## Roadmap
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| Phase | Objectif | État |
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|---|---|---|
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| **0 — Cadrage** | Conception validée | ✅ |
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| **1 — Client voix + HUD** | `stt` : voix locale + HUD + websocket | ✅ |
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| **2 — STT-server** | FastAPI `/v1/ask` → LiteLLM | ✅ |
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| **3 — Déploiement cluster** | image ghcr + manifests k8s + ArgoCD + `litellm-ext` | ✅ (à merger sur `main`) |
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| **4 — HUD avancé** | visualiseur arc-reactor + thèmes + écran réglages | ⏳ |
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| **5 — Mémoire serveur** | Qdrant s01 + distillée GitHub + sessions | ⏳ |
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| **6 — Auto-start client** | `install-service.sh` (systemd --user) + kiosk | ⏳ |
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| **7 — Outils Hermes** | « agir sur Funk » via gateway `:8080` (API à spécifier) | ⏳ |
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### État (testé hors-LAN)
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`py_compile` OK (client + serveur) ; client : `stt --help`, config, `ServerClient` vérifiés ;
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serveur : import/compile OK. Reste à valider sur cible : build image (CI), déploiement ArgoCD
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après merge, et test audio bout-en-bout sur le poste.
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---
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## Projets de référence (inspiration, non forkés)
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Aucun ne se branche proprement sur LiteLLM + Hermes (ils embarquent leur propre LLM/agent),
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mais bons pour le design et le code à piocher :
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- [AlexandreSajus/JARVIS](https://github.com/AlexandreSajus/JARVIS) — Voice→LLM→Speech, interface web (proche du front visé).
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- [InterGenJLU/jarvis](https://github.com/InterGenJLU/jarvis) — AMD ROCm (comme la RX 6700XT), HUD santé, streaming TTS.
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- [novik133/jarvis](https://github.com/novik133/jarvis) — whisper.cpp + Piper (même TTS) + monitoring système.
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- [rhasspy/wyoming-addons](https://github.com/rhasspy/wyoming-addons) — faster-whisper / Piper conteneurisés (option phase 7).
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k8s/apps-of-apps/apps/stt.yaml
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k8s/apps-of-apps/apps/stt.yaml
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apiVersion: argoproj.io/v1alpha1
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kind: Application
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metadata:
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name: stt
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namespace: argocd
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finalizers:
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- resources-finalizer.argocd.argoproj.io
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spec:
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project: default
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source:
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repoURL: git@github.com:Alkatrazz24/Funk-lab.git
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targetRevision: main
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path: k8s/apps/stt
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directory:
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recurse: true
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destination:
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server: https://kubernetes.default.svc
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namespace: ai
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syncPolicy:
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automated:
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prune: true
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selfHeal: true
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syncOptions:
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- CreateNamespace=true
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k8s/apps/stt/deployment.yaml
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k8s/apps/stt/deployment.yaml
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: stt-server
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namespace: ai
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spec:
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replicas: 1
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selector:
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matchLabels:
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app: stt-server
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template:
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metadata:
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labels:
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app: stt-server
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spec:
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containers:
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- name: stt-server
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image: ghcr.io/alkatrazz24/funk-stt-server:latest
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ports:
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- containerPort: 8000
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env:
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# LiteLLM (s01) joint via le Service Endpoints litellm-ext
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- name: STT_LITELLM_URL
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value: "http://litellm-ext:4000/v1/chat/completions"
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- name: STT_LITELLM_KEY
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value: "lm-studio"
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- name: STT_MODEL
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value: "hermes-default"
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readinessProbe:
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httpGet:
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path: /healthz
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port: 8000
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initialDelaySeconds: 5
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periodSeconds: 10
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livenessProbe:
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httpGet:
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path: /healthz
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port: 8000
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initialDelaySeconds: 10
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periodSeconds: 20
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resources:
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requests:
|
||||||
|
cpu: 50m
|
||||||
|
memory: 128Mi
|
||||||
|
limits:
|
||||||
|
cpu: 500m
|
||||||
|
memory: 256Mi
|
||||||
14
k8s/apps/stt/ingress.yaml
Normal file
14
k8s/apps/stt/ingress.yaml
Normal file
|
|
@ -0,0 +1,14 @@
|
||||||
|
apiVersion: traefik.io/v1alpha1
|
||||||
|
kind: IngressRoute
|
||||||
|
metadata:
|
||||||
|
name: stt-server
|
||||||
|
namespace: ai
|
||||||
|
spec:
|
||||||
|
entryPoints:
|
||||||
|
- web
|
||||||
|
routes:
|
||||||
|
- match: Host(`stt.lab.local`)
|
||||||
|
kind: Rule
|
||||||
|
services:
|
||||||
|
- name: stt-server
|
||||||
|
port: 8000
|
||||||
22
k8s/apps/stt/litellm-external.yaml
Normal file
22
k8s/apps/stt/litellm-external.yaml
Normal file
|
|
@ -0,0 +1,22 @@
|
||||||
|
# LiteLLM tourne sur storage-01 (HORS cluster) — on l'expose comme un Service interne
|
||||||
|
# via un Service sans sélecteur + Endpoints manuel pointant vers l'IP LAN cluster de s01.
|
||||||
|
apiVersion: v1
|
||||||
|
kind: Service
|
||||||
|
metadata:
|
||||||
|
name: litellm-ext
|
||||||
|
namespace: ai
|
||||||
|
spec:
|
||||||
|
ports:
|
||||||
|
- port: 4000
|
||||||
|
targetPort: 4000
|
||||||
|
---
|
||||||
|
apiVersion: v1
|
||||||
|
kind: Endpoints
|
||||||
|
metadata:
|
||||||
|
name: litellm-ext
|
||||||
|
namespace: ai
|
||||||
|
subsets:
|
||||||
|
- addresses:
|
||||||
|
- ip: 192.168.10.1 # storage-01, IP LAN cluster (cf. CLAUDE.md)
|
||||||
|
ports:
|
||||||
|
- port: 4000
|
||||||
11
k8s/apps/stt/service.yaml
Normal file
11
k8s/apps/stt/service.yaml
Normal file
|
|
@ -0,0 +1,11 @@
|
||||||
|
apiVersion: v1
|
||||||
|
kind: Service
|
||||||
|
metadata:
|
||||||
|
name: stt-server
|
||||||
|
namespace: ai
|
||||||
|
spec:
|
||||||
|
selector:
|
||||||
|
app: stt-server
|
||||||
|
ports:
|
||||||
|
- port: 8000
|
||||||
|
targetPort: 8000
|
||||||
56
stt/README.md
Normal file
56
stt/README.md
Normal file
|
|
@ -0,0 +1,56 @@
|
||||||
|
# STT — Assistant vocal "Jarvis" de Funk
|
||||||
|
|
||||||
|
Assistant vocal type *Jarvis* pour le homelab Funk, en **architecture client-serveur** :
|
||||||
|
|
||||||
|
- **`server/`** — STT-server : orchestrateur AI **dans le cluster k8s**. Reçoit les requêtes
|
||||||
|
des clients et route l'inférence vers LiteLLM (Qwen3 / Claude). Mémoire centralisée (futur).
|
||||||
|
- **`client/`** — commande `stt` installable sur le poste : capture micro, STT (Whisper),
|
||||||
|
TTS (Piper), **HUD graphique**. Envoie le texte au serveur (URL paramétrable).
|
||||||
|
|
||||||
|
> 📐 Conception complète : `admin/ia/stt.md`.
|
||||||
|
|
||||||
|
```
|
||||||
|
LAN / *.lab.local
|
||||||
|
┌─ POSTE ──────────┐ ┌─ CLUSTER k8s (ns ai) ──────────────┐
|
||||||
|
│ client `stt` │ HTTP │ STT-server (Deployment) │
|
||||||
|
│ • micro + Whisper│ ─────▶ │ POST /v1/ask → LiteLLM │
|
||||||
|
│ • Piper (TTS) │ ◀───── │ (litellm-ext → s01:4000) │
|
||||||
|
│ • HUD web │ reply │ Ingress: stt.lab.local │
|
||||||
|
└───────────────────┘ └──────────────┬──────────────────────┘
|
||||||
|
▼
|
||||||
|
LiteLLM :4000 (s01)
|
||||||
|
→ Qwen3 (g01) / Claude
|
||||||
|
```
|
||||||
|
|
||||||
|
## Client — installation (poste)
|
||||||
|
|
||||||
|
```bash
|
||||||
|
pipx install "git+ssh://git@github.com/Alkatrazz24/Funk-lab.git@<branche>#subdirectory=stt/client"
|
||||||
|
stt --setup # génère ~/.config/stt/stt.toml (dont [server] url)
|
||||||
|
stt # lance la voix + ouvre le HUD ; dis "Ok Hermès …"
|
||||||
|
```
|
||||||
|
Prérequis poste : micro, Piper + voix dans `~/.local/share/piper/`, `aplay` (alsa-utils).
|
||||||
|
|
||||||
|
## Serveur — déploiement (cluster)
|
||||||
|
|
||||||
|
1. **Image** : poussée sur `ghcr.io/alkatrazz24/funk-stt-server` par le workflow
|
||||||
|
`.github/workflows/build-stt-server.yml` (sur push touchant `stt/server/`).
|
||||||
|
2. **ArgoCD** : `k8s/apps-of-apps/apps/stt.yaml` → déploie `k8s/apps/stt/` **depuis `main`**.
|
||||||
|
Tant que ce n'est pas mergé sur `main`, le cluster ne le prend pas.
|
||||||
|
3. Le serveur joint LiteLLM (s01, hors cluster) via le Service `litellm-ext`
|
||||||
|
(Endpoints manuel → `192.168.10.1:4000`).
|
||||||
|
|
||||||
|
## Structure
|
||||||
|
|
||||||
|
```
|
||||||
|
stt/
|
||||||
|
├── client/ # commande `stt` (pipx) — voix + HUD
|
||||||
|
│ ├── pyproject.toml
|
||||||
|
│ ├── config/ # stt.example.toml + thèmes/avatars/voix
|
||||||
|
│ └── stt/ # cli, config, voice/, api.py (→ serveur), ui/ (HUD), hud/
|
||||||
|
└── server/ # STT-server (conteneur) — API + orchestration AI
|
||||||
|
├── pyproject.toml
|
||||||
|
├── Dockerfile
|
||||||
|
├── memory/ # mémoire distillée GitHub (futur, server-side)
|
||||||
|
└── stt_server/ # app.py (FastAPI), brain.py (→ LiteLLM), config.py
|
||||||
|
```
|
||||||
25
stt/client/config/README.md
Normal file
25
stt/client/config/README.md
Normal file
|
|
@ -0,0 +1,25 @@
|
||||||
|
# stt/config — personnalisation
|
||||||
|
|
||||||
|
Tout ce qui se change **sans recompiler**. `stt --setup` copie `stt.example.toml` vers
|
||||||
|
`~/.config/stt/stt.toml`.
|
||||||
|
|
||||||
|
```
|
||||||
|
config/
|
||||||
|
├── stt.example.toml # config de référence (modes cerveau, voix, thème, mémoire)
|
||||||
|
├── themes/ # thèmes HUD : couleurs, fond, style du visualiseur (CSS/JSON)
|
||||||
|
├── avatars/ # images / avatars affichés dans le HUD
|
||||||
|
└── voices/ # modèles Piper .onnx (TTS) — fr_FR par défaut
|
||||||
|
```
|
||||||
|
|
||||||
|
## Points de personnalisation
|
||||||
|
|
||||||
|
| Quoi | Où | Note |
|
||||||
|
|---|---|---|
|
||||||
|
| Image / avatar | `avatars/` + `ui.avatar` | swappable à chaud |
|
||||||
|
| Thème / design | `themes/` + `ui.theme` | couleurs, fond, visualiseur |
|
||||||
|
| Voix | `voices/*.onnx` + `voice.piper_voice` | géré par `stt --setup` |
|
||||||
|
| Wake word | `voice.wake_word` | défaut « Ok Hermès » |
|
||||||
|
| Cerveau | `[brain]` | mode + override + endpoints |
|
||||||
|
|
||||||
|
> 🔒 La clé Anthropic (mode `claude-direct`) passe par la variable d'env `ANTHROPIC_API_KEY`,
|
||||||
|
> **jamais** dans ce fichier (qui peut finir versionné).
|
||||||
28
stt/client/config/stt.example.toml
Normal file
28
stt/client/config/stt.example.toml
Normal file
|
|
@ -0,0 +1,28 @@
|
||||||
|
# Configuration du CLIENT STT — copier vers ~/.config/stt/stt.toml (`stt --setup` le génère).
|
||||||
|
# Reflète les défauts embarqués (stt/client/stt/config.py : DEFAULT_CONFIG).
|
||||||
|
# Le client n'a plus de cerveau : il envoie le texte au STT-server (in-cluster).
|
||||||
|
|
||||||
|
[server]
|
||||||
|
url = "http://stt.lab.local" # endpoint du STT-server (paramétrable)
|
||||||
|
timeout_sec = 90
|
||||||
|
|
||||||
|
[voice]
|
||||||
|
wake_word = "hermes" # "Ok Hermès"
|
||||||
|
whisper_model = "large-v3" # STT faster-whisper (CPU int8) — "small" pour tester
|
||||||
|
piper_voice = "fr_FR-upmc-medium" # TTS — ~/.local/share/piper/<voix>.onnx
|
||||||
|
language = "fr"
|
||||||
|
silence_sec = 1.2
|
||||||
|
min_speech_sec = 0.6
|
||||||
|
chat_timeout_sec = 60
|
||||||
|
|
||||||
|
[ui]
|
||||||
|
theme = "arc-reactor"
|
||||||
|
avatar = "default"
|
||||||
|
kiosk = true
|
||||||
|
ws_host = "127.0.0.1"
|
||||||
|
ws_port = 9300 # websocket états → HUD (doit matcher hud/index.html)
|
||||||
|
http_port = 9301 # HTTP statique qui sert le HUD
|
||||||
|
|
||||||
|
[memory]
|
||||||
|
enabled = true # cache local de conversation (brut, gitignoré)
|
||||||
|
local_db = "~/.local/share/stt/memory.sqlite"
|
||||||
32
stt/client/pyproject.toml
Normal file
32
stt/client/pyproject.toml
Normal file
|
|
@ -0,0 +1,32 @@
|
||||||
|
[project]
|
||||||
|
name = "stt"
|
||||||
|
version = "0.1.0"
|
||||||
|
description = "STT — client vocal Jarvis du homelab Funk (voix + HUD, parle au STT-server)"
|
||||||
|
requires-python = ">=3.11"
|
||||||
|
readme = "README.md"
|
||||||
|
|
||||||
|
# Client : voix locale + HUD. L'inférence est déléguée au STT-server (pas d'anthropic/qdrant ici).
|
||||||
|
dependencies = [
|
||||||
|
"faster-whisper>=1.0.0", # STT local
|
||||||
|
"sounddevice>=0.4.6", # capture micro
|
||||||
|
"numpy>=1.24.0",
|
||||||
|
"requests>=2.31.0", # appels HTTP vers le STT-server
|
||||||
|
"webrtcvad-wheels>=2.0.10",# VAD / wake word
|
||||||
|
"websockets>=12.0", # backend ↔ HUD
|
||||||
|
"tomli-w>=1.0.0", # écriture config TOML
|
||||||
|
]
|
||||||
|
|
||||||
|
[project.scripts]
|
||||||
|
stt = "stt.cli:main"
|
||||||
|
|
||||||
|
[build-system]
|
||||||
|
requires = ["setuptools>=68"]
|
||||||
|
build-backend = "setuptools.build_meta"
|
||||||
|
|
||||||
|
[tool.setuptools.packages.find]
|
||||||
|
where = ["."]
|
||||||
|
include = ["stt*"]
|
||||||
|
|
||||||
|
# Le HUD (assets web) est livré comme données du package stt/
|
||||||
|
[tool.setuptools.package-data]
|
||||||
|
stt = ["hud/*", "hud/**/*"]
|
||||||
24
stt/client/stt/README.md
Normal file
24
stt/client/stt/README.md
Normal file
|
|
@ -0,0 +1,24 @@
|
||||||
|
# stt/stt — package backend
|
||||||
|
|
||||||
|
Cœur Python de la commande `stt`. Réutilise le moteur vocal de `tools/hermes-voice.py`.
|
||||||
|
|
||||||
|
```
|
||||||
|
stt/
|
||||||
|
├── cli.py # entrypoint `stt` (--setup, --mode, --no-tts) ✅ phase 1
|
||||||
|
├── config.py # défauts embarqués + chargement ~/.config/stt/stt.toml ✅ phase 1
|
||||||
|
├── voice/ # wake word + VAD, STT faster-whisper, TTS Piper ✅ phase 1
|
||||||
|
├── brain/ # routeur 3 modes hermes/local/claude + auto-détect LAN ✅ phase 2
|
||||||
|
├── server/ # HTTP statique (HUD) + websocket (états → HUD) ✅ phase 1
|
||||||
|
└── memory/ # tier local SQLite ✅ ; Qdrant s01 + sync GitHub ⏳ phase 4
|
||||||
|
```
|
||||||
|
|
||||||
|
## États exposés au HUD (via websocket)
|
||||||
|
|
||||||
|
`idle` (veille) → `listening` (écoute) → `thinking` (réflexion cerveau) → `speaking` (TTS) → `idle`.
|
||||||
|
Le HUD anime le visualiseur selon l'état et affiche transcript utilisateur + réponse.
|
||||||
|
|
||||||
|
## Point de départ
|
||||||
|
|
||||||
|
Le module `voice/` part de `tools/hermes-voice.py` (déjà fonctionnel : wake word « Ok Hermès »,
|
||||||
|
faster-whisper, Piper, webrtcvad). À refactorer en classe + callbacks d'état pour le websocket,
|
||||||
|
puis brancher `brain/` (routeur) à la place de l'appel direct actuel.
|
||||||
3
stt/client/stt/__init__.py
Normal file
3
stt/client/stt/__init__.py
Normal file
|
|
@ -0,0 +1,3 @@
|
||||||
|
"""STT — assistant vocal Jarvis du homelab Funk."""
|
||||||
|
|
||||||
|
__version__ = "0.0.1"
|
||||||
35
stt/client/stt/api.py
Normal file
35
stt/client/stt/api.py
Normal file
|
|
@ -0,0 +1,35 @@
|
||||||
|
"""Client HTTP vers le STT-server (in-cluster).
|
||||||
|
|
||||||
|
Le client n'a plus de cerveau : il envoie le texte transcrit au serveur, qui
|
||||||
|
orchestre l'inférence (LiteLLM → Qwen3/Claude) et renvoie la réponse.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
from typing import Any
|
||||||
|
|
||||||
|
|
||||||
|
class ServerClient:
|
||||||
|
def __init__(self, cfg: dict[str, Any]):
|
||||||
|
self.url = cfg["url"].rstrip("/")
|
||||||
|
self.timeout = cfg.get("timeout_sec", 90)
|
||||||
|
|
||||||
|
def ask(self, text: str) -> str:
|
||||||
|
import requests
|
||||||
|
|
||||||
|
r = requests.post(
|
||||||
|
f"{self.url}/v1/ask",
|
||||||
|
json={"text": text},
|
||||||
|
timeout=self.timeout,
|
||||||
|
)
|
||||||
|
r.raise_for_status()
|
||||||
|
return r.json()["reply"].strip()
|
||||||
|
|
||||||
|
def healthy(self) -> bool:
|
||||||
|
import requests
|
||||||
|
|
||||||
|
try:
|
||||||
|
r = requests.get(f"{self.url}/healthz", timeout=5)
|
||||||
|
return r.ok
|
||||||
|
except requests.RequestException:
|
||||||
|
return False
|
||||||
70
stt/client/stt/cli.py
Normal file
70
stt/client/stt/cli.py
Normal file
|
|
@ -0,0 +1,70 @@
|
||||||
|
"""Entrypoint de la commande `stt` (client)."""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import asyncio
|
||||||
|
import sys
|
||||||
|
|
||||||
|
|
||||||
|
def _cmd_setup() -> int:
|
||||||
|
from stt.config import write_default_config
|
||||||
|
|
||||||
|
path = write_default_config()
|
||||||
|
print(f"✓ Config : {path}")
|
||||||
|
print("\nÀ installer / configurer sur le poste :")
|
||||||
|
print(" • modèle Whisper (téléchargé au 1er lancement par faster-whisper)")
|
||||||
|
print(" • Piper + voix → ~/.local/share/piper/<voix>.onnx")
|
||||||
|
print(f" • URL du STT-server dans {path} ([server] url)")
|
||||||
|
return 0
|
||||||
|
|
||||||
|
|
||||||
|
def _cmd_run(args) -> int:
|
||||||
|
from stt.api import ServerClient
|
||||||
|
from stt.config import load_config
|
||||||
|
from stt.memory import LocalMemory
|
||||||
|
from stt.ui import serve
|
||||||
|
from stt.voice import VoiceEngine
|
||||||
|
|
||||||
|
cfg = load_config()
|
||||||
|
if args.server:
|
||||||
|
cfg["server"]["url"] = args.server
|
||||||
|
|
||||||
|
client = ServerClient(cfg["server"])
|
||||||
|
print(f"STT-server : {client.url}")
|
||||||
|
if not client.healthy():
|
||||||
|
print(" ⚠️ serveur injoignable — vérifie [server] url et le réseau")
|
||||||
|
|
||||||
|
memory = None
|
||||||
|
if cfg["memory"].get("enabled"):
|
||||||
|
memory = LocalMemory(cfg["memory"]["local_db"])
|
||||||
|
|
||||||
|
def make_engine(emit):
|
||||||
|
return VoiceEngine(
|
||||||
|
cfg["voice"], client.ask, emit, memory=memory, no_tts=args.no_tts
|
||||||
|
)
|
||||||
|
|
||||||
|
try:
|
||||||
|
asyncio.run(serve(cfg, make_engine))
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
print("\n👋 Au revoir")
|
||||||
|
finally:
|
||||||
|
if memory:
|
||||||
|
memory.close()
|
||||||
|
return 0
|
||||||
|
|
||||||
|
|
||||||
|
def main(argv: list[str] | None = None) -> int:
|
||||||
|
p = argparse.ArgumentParser(prog="stt", description="Client vocal Jarvis de Funk")
|
||||||
|
p.add_argument("--setup", action="store_true", help="Écrit la config par défaut")
|
||||||
|
p.add_argument("--server", help="Force l'URL du STT-server (sinon config)")
|
||||||
|
p.add_argument("--no-tts", action="store_true", help="Réponses texte seulement")
|
||||||
|
args = p.parse_args(argv)
|
||||||
|
|
||||||
|
if args.setup:
|
||||||
|
return _cmd_setup()
|
||||||
|
return _cmd_run(args)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
sys.exit(main())
|
||||||
77
stt/client/stt/config.py
Normal file
77
stt/client/stt/config.py
Normal file
|
|
@ -0,0 +1,77 @@
|
||||||
|
"""Configuration du client STT.
|
||||||
|
|
||||||
|
Défauts embarqués (robuste pour pipx). `stt --setup` les écrit dans
|
||||||
|
~/.config/stt/stt.toml ; au lancement on fusionne le fichier utilisateur par-dessus.
|
||||||
|
|
||||||
|
Le client ne contient PLUS le cerveau : il envoie le texte au STT-server (URL paramétrable).
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import tomllib
|
||||||
|
from pathlib import Path
|
||||||
|
from typing import Any
|
||||||
|
|
||||||
|
CONFIG_DIR = Path.home() / ".config" / "stt"
|
||||||
|
CONFIG_PATH = CONFIG_DIR / "stt.toml"
|
||||||
|
DATA_DIR = Path.home() / ".local" / "share" / "stt"
|
||||||
|
|
||||||
|
DEFAULT_CONFIG: dict[str, Any] = {
|
||||||
|
"server": {
|
||||||
|
# STT-server (in-cluster) — joignable via Traefik / wildcard *.lab.local
|
||||||
|
"url": "http://stt.lab.local",
|
||||||
|
"timeout_sec": 90,
|
||||||
|
},
|
||||||
|
"voice": {
|
||||||
|
"wake_word": "hermes",
|
||||||
|
"whisper_model": "large-v3",
|
||||||
|
"piper_voice": "fr_FR-upmc-medium",
|
||||||
|
"language": "fr",
|
||||||
|
"silence_sec": 1.2,
|
||||||
|
"min_speech_sec": 0.6,
|
||||||
|
"chat_timeout_sec": 60,
|
||||||
|
},
|
||||||
|
"ui": {
|
||||||
|
"theme": "arc-reactor",
|
||||||
|
"avatar": "default",
|
||||||
|
"kiosk": True,
|
||||||
|
"ws_host": "127.0.0.1",
|
||||||
|
"ws_port": 9300,
|
||||||
|
"http_port": 9301,
|
||||||
|
},
|
||||||
|
"memory": {
|
||||||
|
# cache local de conversation (brut, gitignoré) — la vraie mémoire est côté serveur
|
||||||
|
"enabled": True,
|
||||||
|
"local_db": str(DATA_DIR / "memory.sqlite"),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def _deep_merge(base: dict, over: dict) -> dict:
|
||||||
|
out = dict(base)
|
||||||
|
for k, v in over.items():
|
||||||
|
if isinstance(v, dict) and isinstance(out.get(k), dict):
|
||||||
|
out[k] = _deep_merge(out[k], v)
|
||||||
|
else:
|
||||||
|
out[k] = v
|
||||||
|
return out
|
||||||
|
|
||||||
|
|
||||||
|
def load_config() -> dict[str, Any]:
|
||||||
|
if CONFIG_PATH.exists():
|
||||||
|
with CONFIG_PATH.open("rb") as f:
|
||||||
|
user = tomllib.load(f)
|
||||||
|
return _deep_merge(DEFAULT_CONFIG, user)
|
||||||
|
return DEFAULT_CONFIG
|
||||||
|
|
||||||
|
|
||||||
|
def write_default_config(force: bool = False) -> Path:
|
||||||
|
import tomli_w
|
||||||
|
|
||||||
|
CONFIG_DIR.mkdir(parents=True, exist_ok=True)
|
||||||
|
DATA_DIR.mkdir(parents=True, exist_ok=True)
|
||||||
|
if CONFIG_PATH.exists() and not force:
|
||||||
|
return CONFIG_PATH
|
||||||
|
with CONFIG_PATH.open("wb") as f:
|
||||||
|
tomli_w.dump(DEFAULT_CONFIG, f)
|
||||||
|
return CONFIG_PATH
|
||||||
20
stt/client/stt/hud/README.md
Normal file
20
stt/client/stt/hud/README.md
Normal file
|
|
@ -0,0 +1,20 @@
|
||||||
|
# stt/hud — interface HUD web (à implémenter, phase 3)
|
||||||
|
|
||||||
|
Front statique servi localement par le backend, ouvert en kiosk (`ui.kiosk = true`).
|
||||||
|
Se connecte au serveur websocket du backend.
|
||||||
|
|
||||||
|
## Contenu prévu
|
||||||
|
|
||||||
|
- **Visualiseur** : canvas/WebGL animé (arc-reactor / waveform) réagissant aux états
|
||||||
|
`idle / listening / thinking / speaking`.
|
||||||
|
- **Conversation** : transcript utilisateur + réponses de l'assistant.
|
||||||
|
- **Réglages** : thème, avatar, voix, wake word, mode cerveau — écrit dans `~/.config/stt/stt.toml`.
|
||||||
|
|
||||||
|
## Design
|
||||||
|
|
||||||
|
S'inspire de [AlexandreSajus/JARVIS](https://github.com/AlexandreSajus/JARVIS) (interface web)
|
||||||
|
et de l'esthétique Iron Man. Thèmes pilotés par `stt/config/themes/`, avatar par
|
||||||
|
`stt/config/avatars/`.
|
||||||
|
|
||||||
|
Pas de framework lourd imposé — HTML/JS + canvas suffit pour le MVP. Le choix (vanilla vs
|
||||||
|
petit framework) sera tranché en phase 3.
|
||||||
82
stt/client/stt/hud/index.html
Normal file
82
stt/client/stt/hud/index.html
Normal file
|
|
@ -0,0 +1,82 @@
|
||||||
|
<!doctype html>
|
||||||
|
<html lang="fr">
|
||||||
|
<head>
|
||||||
|
<meta charset="utf-8" />
|
||||||
|
<meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||||
|
<title>STT — Funk</title>
|
||||||
|
<style>
|
||||||
|
:root { --accent:#36d1ff; --bg:#04080f; --dim:#1b2a3a; }
|
||||||
|
* { box-sizing:border-box; }
|
||||||
|
body {
|
||||||
|
margin:0; height:100vh; background:radial-gradient(circle at 50% 40%, #0a1626, var(--bg));
|
||||||
|
color:#cfe9ff; font-family:system-ui, sans-serif; overflow:hidden;
|
||||||
|
display:flex; flex-direction:column; align-items:center; justify-content:center;
|
||||||
|
}
|
||||||
|
#core {
|
||||||
|
width:240px; height:240px; border-radius:50%; position:relative;
|
||||||
|
display:flex; align-items:center; justify-content:center; transition:all .4s;
|
||||||
|
box-shadow:0 0 60px -10px var(--accent);
|
||||||
|
}
|
||||||
|
#core::before, #core::after {
|
||||||
|
content:""; position:absolute; border-radius:50%; border:2px solid var(--accent);
|
||||||
|
inset:0; opacity:.5; animation:pulse 3s ease-in-out infinite;
|
||||||
|
}
|
||||||
|
#core::after { inset:30px; opacity:.25; animation-delay:.6s; }
|
||||||
|
#state { font-size:1.1rem; letter-spacing:.25em; text-transform:uppercase; }
|
||||||
|
.idle #core { box-shadow:0 0 50px -15px var(--accent); filter:saturate(.6); }
|
||||||
|
.listening #core { box-shadow:0 0 80px 0 var(--accent); }
|
||||||
|
.thinking #core { box-shadow:0 0 70px 0 #ffd23f; }
|
||||||
|
.speaking #core { box-shadow:0 0 90px 5px #36ff9e; }
|
||||||
|
@keyframes pulse { 0%,100%{transform:scale(1);opacity:.5} 50%{transform:scale(1.08);opacity:.15} }
|
||||||
|
#log {
|
||||||
|
position:absolute; bottom:0; left:0; right:0; max-height:32vh; overflow:auto;
|
||||||
|
padding:1rem 1.5rem; font-size:.95rem; line-height:1.5; backdrop-filter:blur(4px);
|
||||||
|
}
|
||||||
|
.turn { margin:.3rem 0; }
|
||||||
|
.user { color:#9fd0ff; }
|
||||||
|
.asst { color:#aef5cf; }
|
||||||
|
.err { color:#ff8c8c; }
|
||||||
|
.tag { opacity:.5; font-size:.75rem; margin-left:.5rem; }
|
||||||
|
#conn { position:absolute; top:12px; right:16px; font-size:.75rem; opacity:.6; }
|
||||||
|
</style>
|
||||||
|
</head>
|
||||||
|
<body class="idle">
|
||||||
|
<div id="conn">●</div>
|
||||||
|
<div id="core"></div>
|
||||||
|
<div id="state">veille</div>
|
||||||
|
<div id="log"></div>
|
||||||
|
|
||||||
|
<script>
|
||||||
|
const WS_PORT = 9300; // doit matcher ui.ws_port
|
||||||
|
const LABELS = { idle:"veille", listening:"écoute", thinking:"réflexion", speaking:"réponse" };
|
||||||
|
const body = document.body, stateEl = document.getElementById("state");
|
||||||
|
const logEl = document.getElementById("log"), connEl = document.getElementById("conn");
|
||||||
|
|
||||||
|
function setState(s) {
|
||||||
|
body.className = s;
|
||||||
|
stateEl.textContent = LABELS[s] || s;
|
||||||
|
}
|
||||||
|
function addTurn(cls, text, tag) {
|
||||||
|
const d = document.createElement("div");
|
||||||
|
d.className = "turn " + cls;
|
||||||
|
d.textContent = text;
|
||||||
|
if (tag) { const t = document.createElement("span"); t.className="tag"; t.textContent=tag; d.appendChild(t); }
|
||||||
|
logEl.appendChild(d);
|
||||||
|
logEl.scrollTop = logEl.scrollHeight;
|
||||||
|
}
|
||||||
|
function connect() {
|
||||||
|
const ws = new WebSocket(`ws://${location.hostname}:${WS_PORT}`);
|
||||||
|
ws.onopen = () => connEl.style.color = "#36ff9e";
|
||||||
|
ws.onclose = () => { connEl.style.color = "#ff8c8c"; setTimeout(connect, 1500); };
|
||||||
|
ws.onmessage = (e) => {
|
||||||
|
const ev = JSON.parse(e.data);
|
||||||
|
if (ev.type === "state") setState(ev.state);
|
||||||
|
else if (ev.type === "user") addTurn("user", "🗣️ " + ev.text);
|
||||||
|
else if (ev.type === "assistant") addTurn("asst", "🤖 " + ev.text, ev.mode);
|
||||||
|
else if (ev.type === "error") addTurn("err", "⚠️ " + ev.text);
|
||||||
|
};
|
||||||
|
}
|
||||||
|
connect();
|
||||||
|
</script>
|
||||||
|
</body>
|
||||||
|
</html>
|
||||||
8
stt/client/stt/memory/__init__.py
Normal file
8
stt/client/stt/memory/__init__.py
Normal file
|
|
@ -0,0 +1,8 @@
|
||||||
|
"""Mémoire STT — tier local (SQLite) en phase 1.
|
||||||
|
|
||||||
|
Tiers Qdrant (s01) et distillation/sync GitHub : phase 4 (voir admin/ia/stt.md).
|
||||||
|
"""
|
||||||
|
|
||||||
|
from stt.memory.store import LocalMemory
|
||||||
|
|
||||||
|
__all__ = ["LocalMemory"]
|
||||||
49
stt/client/stt/memory/store.py
Normal file
49
stt/client/stt/memory/store.py
Normal file
|
|
@ -0,0 +1,49 @@
|
||||||
|
"""Tier mémoire local : journal de conversation en SQLite (brut, gitignoré).
|
||||||
|
|
||||||
|
Phase 1 : on persiste juste les tours de conversation. La distillation vers GitHub
|
||||||
|
et l'embedding Qdrant (s01) arrivent en phase 4.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import sqlite3
|
||||||
|
import time
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
|
||||||
|
class LocalMemory:
|
||||||
|
def __init__(self, db_path: str):
|
||||||
|
self.path = Path(db_path).expanduser()
|
||||||
|
self.path.parent.mkdir(parents=True, exist_ok=True)
|
||||||
|
self._db = sqlite3.connect(str(self.path), check_same_thread=False)
|
||||||
|
self._db.execute(
|
||||||
|
"""CREATE TABLE IF NOT EXISTS turns (
|
||||||
|
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||||
|
ts REAL NOT NULL,
|
||||||
|
role TEXT NOT NULL,
|
||||||
|
content TEXT NOT NULL,
|
||||||
|
mode TEXT
|
||||||
|
)"""
|
||||||
|
)
|
||||||
|
self._db.commit()
|
||||||
|
|
||||||
|
def log(self, role: str, content: str, mode: str | None = None) -> None:
|
||||||
|
self._db.execute(
|
||||||
|
"INSERT INTO turns (ts, role, content, mode) VALUES (?, ?, ?, ?)",
|
||||||
|
(time.time(), role, content, mode),
|
||||||
|
)
|
||||||
|
self._db.commit()
|
||||||
|
|
||||||
|
def recent(self, limit: int = 20) -> list[dict]:
|
||||||
|
cur = self._db.execute(
|
||||||
|
"SELECT ts, role, content, mode FROM turns ORDER BY id DESC LIMIT ?",
|
||||||
|
(limit,),
|
||||||
|
)
|
||||||
|
rows = [
|
||||||
|
{"ts": ts, "role": role, "content": content, "mode": mode}
|
||||||
|
for ts, role, content, mode in cur.fetchall()
|
||||||
|
]
|
||||||
|
return list(reversed(rows))
|
||||||
|
|
||||||
|
def close(self) -> None:
|
||||||
|
self._db.close()
|
||||||
5
stt/client/stt/ui/__init__.py
Normal file
5
stt/client/stt/ui/__init__.py
Normal file
|
|
@ -0,0 +1,5 @@
|
||||||
|
"""UI locale : sert le HUD (HTTP statique) + diffuse les états (websocket)."""
|
||||||
|
|
||||||
|
from stt.ui.app import serve
|
||||||
|
|
||||||
|
__all__ = ["serve"]
|
||||||
96
stt/client/stt/ui/app.py
Normal file
96
stt/client/stt/ui/app.py
Normal file
|
|
@ -0,0 +1,96 @@
|
||||||
|
"""Serveur asyncio : sert le HUD (HTTP statique) et diffuse les états (websocket).
|
||||||
|
|
||||||
|
Le moteur vocal tourne dans un thread (audio bloquant) et pousse ses événements via
|
||||||
|
`emit()`, relayés sur la boucle asyncio puis diffusés à tous les clients HUD connectés.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
import threading
|
||||||
|
from functools import partial
|
||||||
|
from http.server import SimpleHTTPRequestHandler, ThreadingHTTPServer
|
||||||
|
from pathlib import Path
|
||||||
|
from typing import Any
|
||||||
|
|
||||||
|
# Le HUD est embarqué DANS le package (stt/hud) pour être livré par pipx.
|
||||||
|
HUD_DIR = Path(__file__).resolve().parent.parent / "hud"
|
||||||
|
|
||||||
|
|
||||||
|
def _start_http(host: str, port: int) -> ThreadingHTTPServer:
|
||||||
|
handler = partial(SimpleHTTPRequestHandler, directory=str(HUD_DIR))
|
||||||
|
httpd = ThreadingHTTPServer((host, port), handler)
|
||||||
|
threading.Thread(target=httpd.serve_forever, daemon=True).start()
|
||||||
|
return httpd
|
||||||
|
|
||||||
|
|
||||||
|
async def serve(cfg: dict[str, Any], make_engine) -> None:
|
||||||
|
"""make_engine(emit) -> VoiceEngine. emit est thread-safe (depuis le thread voix)."""
|
||||||
|
import websockets
|
||||||
|
|
||||||
|
ui = cfg["ui"]
|
||||||
|
loop = asyncio.get_running_loop()
|
||||||
|
clients: set = set()
|
||||||
|
last_state = {"type": "state", "state": "idle"}
|
||||||
|
|
||||||
|
def broadcast(event: dict) -> None:
|
||||||
|
nonlocal last_state
|
||||||
|
if event.get("type") == "state":
|
||||||
|
last_state = event
|
||||||
|
data = json.dumps(event, ensure_ascii=False)
|
||||||
|
for ws in list(clients):
|
||||||
|
asyncio.create_task(_safe_send(ws, data))
|
||||||
|
|
||||||
|
async def _safe_send(ws, data: str) -> None:
|
||||||
|
try:
|
||||||
|
await ws.send(data)
|
||||||
|
except Exception:
|
||||||
|
clients.discard(ws)
|
||||||
|
|
||||||
|
# emit appelé depuis le thread voix → on repasse sur la boucle asyncio
|
||||||
|
def emit(event: dict) -> None:
|
||||||
|
loop.call_soon_threadsafe(broadcast, event)
|
||||||
|
|
||||||
|
async def handler(ws):
|
||||||
|
clients.add(ws)
|
||||||
|
try:
|
||||||
|
await ws.send(json.dumps(last_state, ensure_ascii=False))
|
||||||
|
async for _ in ws: # le HUD n'envoie rien pour l'instant (phase 5 : réglages)
|
||||||
|
pass
|
||||||
|
finally:
|
||||||
|
clients.discard(ws)
|
||||||
|
|
||||||
|
_start_http(ui["ws_host"], ui["http_port"])
|
||||||
|
|
||||||
|
engine = make_engine(emit)
|
||||||
|
# chargement Whisper + boucle audio dans un thread dédié
|
||||||
|
threading.Thread(target=_run_engine, args=(engine, emit), daemon=True).start()
|
||||||
|
|
||||||
|
async with websockets.serve(handler, ui["ws_host"], ui["ws_port"]):
|
||||||
|
url = f"http://{ui['ws_host']}:{ui['http_port']}/"
|
||||||
|
print(f" HUD : {url}")
|
||||||
|
if ui.get("kiosk"):
|
||||||
|
_open_browser(url, kiosk=True)
|
||||||
|
await asyncio.Future() # tourne indéfiniment
|
||||||
|
|
||||||
|
|
||||||
|
def _run_engine(engine, emit) -> None:
|
||||||
|
try:
|
||||||
|
engine.load()
|
||||||
|
engine.run()
|
||||||
|
except Exception as e: # noqa: BLE001
|
||||||
|
emit({"type": "error", "text": f"moteur vocal : {e}"})
|
||||||
|
|
||||||
|
|
||||||
|
def _open_browser(url: str, kiosk: bool) -> None:
|
||||||
|
import shutil
|
||||||
|
import subprocess
|
||||||
|
import webbrowser
|
||||||
|
|
||||||
|
for browser in ("chromium", "google-chrome", "chromium-browser"):
|
||||||
|
if shutil.which(browser):
|
||||||
|
flag = "--kiosk" if kiosk else "--new-window"
|
||||||
|
subprocess.Popen([browser, flag, url])
|
||||||
|
return
|
||||||
|
webbrowser.open(url)
|
||||||
5
stt/client/stt/voice/__init__.py
Normal file
5
stt/client/stt/voice/__init__.py
Normal file
|
|
@ -0,0 +1,5 @@
|
||||||
|
"""Moteur vocal — wake word, STT (faster-whisper), TTS (Piper)."""
|
||||||
|
|
||||||
|
from stt.voice.engine import VoiceEngine
|
||||||
|
|
||||||
|
__all__ = ["VoiceEngine"]
|
||||||
233
stt/client/stt/voice/engine.py
Normal file
233
stt/client/stt/voice/engine.py
Normal file
|
|
@ -0,0 +1,233 @@
|
||||||
|
"""Moteur vocal STT.
|
||||||
|
|
||||||
|
Refactor de tools/hermes-voice.py en classe avec callbacks d'état (`emit`), pour que
|
||||||
|
le serveur websocket puisse pousser l'état au HUD. Boucle :
|
||||||
|
veille → wake word "Ok Hermès" → conversation (parole libre) → retour veille.
|
||||||
|
|
||||||
|
Dépendances lourdes (sounddevice, faster_whisper, webrtcvad) importées à la volée :
|
||||||
|
`stt --help` et la config fonctionnent sans micro ni modèle installé.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import queue
|
||||||
|
import subprocess
|
||||||
|
import tempfile
|
||||||
|
import time
|
||||||
|
import unicodedata
|
||||||
|
from collections import deque
|
||||||
|
from pathlib import Path
|
||||||
|
from typing import Any, Callable
|
||||||
|
|
||||||
|
SAMPLE_RATE = 16000
|
||||||
|
PRE_ROLL = 15
|
||||||
|
CHAT_EXIT = {"au revoir", "bonne nuit", "stop hermes", "fin"}
|
||||||
|
|
||||||
|
PIPER_BIN = Path.home() / ".local/share/piper-runtime/piper"
|
||||||
|
PIPER_VOICE_DIR = Path.home() / ".local/share/piper"
|
||||||
|
|
||||||
|
|
||||||
|
def _deaccent(s: str) -> str:
|
||||||
|
return (
|
||||||
|
unicodedata.normalize("NFD", s)
|
||||||
|
.encode("ascii", "ignore")
|
||||||
|
.decode("ascii")
|
||||||
|
.lower()
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class VoiceEngine:
|
||||||
|
"""Écoute le micro, transcrit, interroge le STT-server, synthétise la réponse.
|
||||||
|
|
||||||
|
`responder(text) -> str` envoie le texte au serveur et renvoie la réponse.
|
||||||
|
`emit(event: dict)` est appelé à chaque changement d'état / message, pour le HUD.
|
||||||
|
Événements : {"type": "state", "state": idle|listening|thinking|speaking},
|
||||||
|
{"type": "user", "text": …}, {"type": "assistant", "text": …},
|
||||||
|
{"type": "error", "text": …}.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
cfg: dict[str, Any],
|
||||||
|
responder: Callable[[str], str],
|
||||||
|
emit: Callable[[dict], None],
|
||||||
|
memory=None,
|
||||||
|
no_tts: bool = False,
|
||||||
|
):
|
||||||
|
self.cfg = cfg
|
||||||
|
self.responder = responder
|
||||||
|
self.emit = emit
|
||||||
|
self.memory = memory
|
||||||
|
self.no_tts = no_tts
|
||||||
|
self._whisper = None
|
||||||
|
self._stop = False
|
||||||
|
|
||||||
|
# ── chargement modèle ────────────────────────────────────────────────
|
||||||
|
def load(self) -> None:
|
||||||
|
from faster_whisper import WhisperModel
|
||||||
|
|
||||||
|
self._whisper = WhisperModel(
|
||||||
|
self.cfg["whisper_model"], device="cpu", compute_type="int8"
|
||||||
|
)
|
||||||
|
|
||||||
|
# ── STT ──────────────────────────────────────────────────────────────
|
||||||
|
def _transcribe(self, audio) -> str:
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
audio_f32 = audio.astype(np.float32) / 32768.0
|
||||||
|
segments, _ = self._whisper.transcribe(
|
||||||
|
audio_f32,
|
||||||
|
language=self.cfg.get("language", "fr"),
|
||||||
|
task="transcribe",
|
||||||
|
beam_size=3,
|
||||||
|
vad_filter=True,
|
||||||
|
initial_prompt="Transcription en français.",
|
||||||
|
)
|
||||||
|
return " ".join(seg.text for seg in segments).strip()
|
||||||
|
|
||||||
|
def _detect_wake(self, text: str) -> tuple[bool, str]:
|
||||||
|
wake = self.cfg.get("wake_word", "hermes")
|
||||||
|
words = text.strip().split()
|
||||||
|
low = [_deaccent(w).strip(",.!?«»") for w in words]
|
||||||
|
try:
|
||||||
|
idx = next(i for i, w in enumerate(low) if wake in w)
|
||||||
|
return True, " ".join(words[idx + 1:]).strip()
|
||||||
|
except StopIteration:
|
||||||
|
return False, ""
|
||||||
|
|
||||||
|
# ── TTS ──────────────────────────────────────────────────────────────
|
||||||
|
def _speak(self, text: str) -> None:
|
||||||
|
if self.no_tts:
|
||||||
|
return
|
||||||
|
voice = PIPER_VOICE_DIR / f"{self.cfg['piper_voice']}.onnx"
|
||||||
|
if not voice.exists() or not PIPER_BIN.exists():
|
||||||
|
return
|
||||||
|
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
|
||||||
|
out = Path(f.name)
|
||||||
|
try:
|
||||||
|
subprocess.run(
|
||||||
|
[str(PIPER_BIN), "--model", str(voice), "--output_file", str(out)],
|
||||||
|
input=text.encode(),
|
||||||
|
capture_output=True,
|
||||||
|
check=True,
|
||||||
|
env={"LD_LIBRARY_PATH": str(PIPER_BIN.parent)},
|
||||||
|
)
|
||||||
|
subprocess.run(["aplay", "-q", str(out)], check=False)
|
||||||
|
except FileNotFoundError:
|
||||||
|
pass
|
||||||
|
finally:
|
||||||
|
out.unlink(missing_ok=True)
|
||||||
|
|
||||||
|
# ── interrogation du serveur ─────────────────────────────────────────
|
||||||
|
def _respond(self, text: str) -> None:
|
||||||
|
self.emit({"type": "user", "text": text})
|
||||||
|
if self.memory:
|
||||||
|
self.memory.log("user", text)
|
||||||
|
self.emit({"type": "state", "state": "thinking"})
|
||||||
|
try:
|
||||||
|
resp = self.responder(text)
|
||||||
|
except Exception as e: # noqa: BLE001 — on remonte au HUD, on ne crash pas
|
||||||
|
self.emit({"type": "error", "text": str(e)})
|
||||||
|
self.emit({"type": "state", "state": "idle"})
|
||||||
|
return
|
||||||
|
self.emit({"type": "assistant", "text": resp})
|
||||||
|
if self.memory:
|
||||||
|
self.memory.log("assistant", resp)
|
||||||
|
self.emit({"type": "state", "state": "speaking"})
|
||||||
|
self._speak(resp)
|
||||||
|
|
||||||
|
# ── boucle principale (VAD + wake word) ──────────────────────────────
|
||||||
|
def run(self) -> None:
|
||||||
|
import numpy as np
|
||||||
|
import sounddevice as sd
|
||||||
|
import webrtcvad
|
||||||
|
|
||||||
|
vad = webrtcvad.Vad(mode=3)
|
||||||
|
frame_ms = 20
|
||||||
|
frame_samples = int(SAMPLE_RATE * frame_ms / 1000)
|
||||||
|
silence_sec = self.cfg.get("silence_sec", 1.2)
|
||||||
|
min_speech = self.cfg.get("min_speech_sec", 0.6)
|
||||||
|
chat_timeout = self.cfg.get("chat_timeout_sec", 60)
|
||||||
|
required_silent = int(silence_sec * 1000 / frame_ms)
|
||||||
|
|
||||||
|
audio_q: queue.Queue = queue.Queue()
|
||||||
|
pre_roll: deque = deque(maxlen=PRE_ROLL)
|
||||||
|
chat_mode = False
|
||||||
|
last_activity = 0.0
|
||||||
|
|
||||||
|
def callback(indata, frames, time_info, status):
|
||||||
|
chunk = (indata[:, 0] if indata.ndim > 1 else indata.flatten()).copy()
|
||||||
|
audio_q.put(chunk)
|
||||||
|
|
||||||
|
def record_until_silence() -> "np.ndarray | None":
|
||||||
|
captured, silent = [], 0
|
||||||
|
while not self._stop:
|
||||||
|
try:
|
||||||
|
frame = audio_q.get(timeout=3.0)
|
||||||
|
except queue.Empty:
|
||||||
|
break
|
||||||
|
captured.append(frame)
|
||||||
|
try:
|
||||||
|
speech = vad.is_speech(frame.tobytes(), SAMPLE_RATE)
|
||||||
|
except Exception:
|
||||||
|
speech = True
|
||||||
|
silent = 0 if speech else silent + 1
|
||||||
|
if silent >= required_silent:
|
||||||
|
break
|
||||||
|
if not captured:
|
||||||
|
return None
|
||||||
|
audio = np.concatenate(captured)
|
||||||
|
return audio if len(audio) / SAMPLE_RATE >= min_speech else None
|
||||||
|
|
||||||
|
self.emit({"type": "state", "state": "idle"})
|
||||||
|
with sd.InputStream(
|
||||||
|
samplerate=SAMPLE_RATE, channels=1, dtype="int16",
|
||||||
|
blocksize=frame_samples, callback=callback,
|
||||||
|
):
|
||||||
|
while not self._stop:
|
||||||
|
if chat_mode and time.time() - last_activity > chat_timeout:
|
||||||
|
chat_mode = False
|
||||||
|
self.emit({"type": "state", "state": "idle"})
|
||||||
|
|
||||||
|
frame = audio_q.get()
|
||||||
|
pre_roll.append(frame)
|
||||||
|
try:
|
||||||
|
if not vad.is_speech(frame.tobytes(), SAMPLE_RATE):
|
||||||
|
continue
|
||||||
|
except Exception:
|
||||||
|
continue
|
||||||
|
|
||||||
|
self.emit({"type": "state", "state": "listening"})
|
||||||
|
captured = list(pre_roll)
|
||||||
|
tail = record_until_silence()
|
||||||
|
if tail is None:
|
||||||
|
self.emit({"type": "state", "state": "idle" if not chat_mode else "listening"})
|
||||||
|
continue
|
||||||
|
audio = np.concatenate([np.concatenate(captured), tail])
|
||||||
|
text = self._transcribe(audio)
|
||||||
|
if not text:
|
||||||
|
continue
|
||||||
|
|
||||||
|
if chat_mode:
|
||||||
|
if any(w in _deaccent(text) for w in CHAT_EXIT):
|
||||||
|
chat_mode = False
|
||||||
|
self.emit({"type": "user", "text": text})
|
||||||
|
self._speak("À bientôt !")
|
||||||
|
self.emit({"type": "state", "state": "idle"})
|
||||||
|
continue
|
||||||
|
last_activity = time.time()
|
||||||
|
self._respond(text)
|
||||||
|
self.emit({"type": "state", "state": "listening"})
|
||||||
|
else:
|
||||||
|
found, command = self._detect_wake(text)
|
||||||
|
if not found:
|
||||||
|
self.emit({"type": "state", "state": "idle"})
|
||||||
|
continue
|
||||||
|
chat_mode = True
|
||||||
|
last_activity = time.time()
|
||||||
|
if command:
|
||||||
|
self._respond(command)
|
||||||
|
self.emit({"type": "state", "state": "listening"})
|
||||||
|
|
||||||
|
def stop(self) -> None:
|
||||||
|
self._stop = True
|
||||||
10
stt/server/Dockerfile
Normal file
10
stt/server/Dockerfile
Normal file
|
|
@ -0,0 +1,10 @@
|
||||||
|
FROM python:3.12-slim
|
||||||
|
|
||||||
|
WORKDIR /app
|
||||||
|
COPY pyproject.toml ./
|
||||||
|
COPY stt_server ./stt_server
|
||||||
|
RUN pip install --no-cache-dir .
|
||||||
|
|
||||||
|
EXPOSE 8000
|
||||||
|
# Service interne au cluster (exposé via Traefik). uvicorn multi-workers.
|
||||||
|
CMD ["uvicorn", "stt_server.app:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "2"]
|
||||||
44
stt/server/README.md
Normal file
44
stt/server/README.md
Normal file
|
|
@ -0,0 +1,44 @@
|
||||||
|
# STT-server — orchestrateur AI (in-cluster)
|
||||||
|
|
||||||
|
API FastAPI déployée dans le cluster k8s (namespace `ai`). Reçoit les requêtes des clients
|
||||||
|
STT et route l'inférence vers LiteLLM (s01) → Qwen3 (g01) / Claude.
|
||||||
|
|
||||||
|
## API
|
||||||
|
|
||||||
|
| Méthode | Route | Corps | Réponse |
|
||||||
|
|---|---|---|---|
|
||||||
|
| `GET` | `/healthz` | — | `{status, version}` |
|
||||||
|
| `POST` | `/v1/ask` | `{text}` | `{reply}` |
|
||||||
|
|
||||||
|
## Configuration (variables d'env)
|
||||||
|
|
||||||
|
| Var | Défaut | Rôle |
|
||||||
|
|---|---|---|
|
||||||
|
| `STT_LITELLM_URL` | `http://litellm-ext:4000/v1/chat/completions` | endpoint LiteLLM (via Service Endpoints) |
|
||||||
|
| `STT_LITELLM_KEY` | `lm-studio` | clé LiteLLM (valeur exacte attendue) |
|
||||||
|
| `STT_MODEL` | `hermes-default` | alias LiteLLM (bascule Qwen/Claude via `hermes-switch`) |
|
||||||
|
| `STT_SYSTEM_PROMPT` | prompt vocal FR concis | persona |
|
||||||
|
| `STT_MAX_TOKENS` / `STT_TEMPERATURE` | `200` / `0.7` | génération |
|
||||||
|
|
||||||
|
## Dev local
|
||||||
|
|
||||||
|
```bash
|
||||||
|
cd stt/server
|
||||||
|
pip install -e .
|
||||||
|
STT_LITELLM_URL=http://192.168.1.200:4000/v1/chat/completions stt-server # :8000
|
||||||
|
curl -s localhost:8000/healthz
|
||||||
|
curl -s localhost:8000/v1/ask -H 'content-type: application/json' -d '{"text":"bonjour"}'
|
||||||
|
```
|
||||||
|
|
||||||
|
## Déploiement
|
||||||
|
|
||||||
|
- Image construite/poussée par `.github/workflows/build-stt-server.yml` → `ghcr.io/alkatrazz24/funk-stt-server`.
|
||||||
|
- Manifests : `k8s/apps/stt/` ; Application ArgoCD : `k8s/apps-of-apps/apps/stt.yaml` (depuis `main`).
|
||||||
|
- Accès LiteLLM (hors cluster) : Service + Endpoints `litellm-ext` → `192.168.10.1:4000`.
|
||||||
|
|
||||||
|
## À venir
|
||||||
|
|
||||||
|
- Intégration des **outils Hermes** (« agir sur Funk ») via le gateway `:8080` — nécessite
|
||||||
|
de spécifier son API. Aujourd'hui : inférence/chat seulement (via LiteLLM).
|
||||||
|
- **Mémoire** centralisée : Qdrant (s01) + distillation versionnée (`server/memory/`).
|
||||||
|
- Sessions multi-clients + historique.
|
||||||
5
stt/server/memory/.gitignore
vendored
Normal file
5
stt/server/memory/.gitignore
vendored
Normal file
|
|
@ -0,0 +1,5 @@
|
||||||
|
# Mémoire brute — ne quitte jamais le poste
|
||||||
|
raw/
|
||||||
|
*.sqlite
|
||||||
|
*.sqlite-*
|
||||||
|
*.db
|
||||||
26
stt/server/memory/README.md
Normal file
26
stt/server/memory/README.md
Normal file
|
|
@ -0,0 +1,26 @@
|
||||||
|
# stt/memory — mémoire 3 tiers
|
||||||
|
|
||||||
|
```
|
||||||
|
memory/
|
||||||
|
├── distilled/ # mémoire DISTILLÉE — versionnée sur GitHub (faits, préférences, résumés)
|
||||||
|
└── raw/ # historique BRUT local — gitignoré, ne quitte jamais le poste
|
||||||
|
```
|
||||||
|
|
||||||
|
## Les 3 tiers
|
||||||
|
|
||||||
|
| Tier | Support | Contenu | Versionné ? |
|
||||||
|
|---|---|---|---|
|
||||||
|
| **Local** | SQLite (`~/.local/share/stt/`) + `raw/` | Historique brut, working memory | ❌ gitignoré |
|
||||||
|
| **s01** | Qdrant `:6333` (collection `stt-memory`) | Sémantique long-terme, partagée RAG Funk | — (sur s01) |
|
||||||
|
| **GitHub** | `distilled/` | Faits, préférences, résumés distillés | ✅ committé |
|
||||||
|
|
||||||
|
## Synchro
|
||||||
|
|
||||||
|
1. **Démarrage** → `git pull` `distilled/` → charge en local.
|
||||||
|
2. **Usage** → écriture immédiate en SQLite local.
|
||||||
|
3. **Arrêt / périodique** → distillation (résumé via le cerveau) → `git commit` `distilled/`
|
||||||
|
**+** embedding dans Qdrant s01 (si LAN).
|
||||||
|
4. **Nouveau poste** → `distilled/` re-seed le local + Qdrant.
|
||||||
|
|
||||||
|
> ⚠️ **Vie privée** : ne committer QUE de la mémoire distillée (pas de transcripts bruts).
|
||||||
|
> `raw/` et la SQLite sont gitignorés. Le repo doit rester privé.
|
||||||
1
stt/server/memory/distilled/.gitkeep
Normal file
1
stt/server/memory/distilled/.gitkeep
Normal file
|
|
@ -0,0 +1 @@
|
||||||
|
# Mémoire distillée versionnée (faits, préférences, résumés). Rempli par le backend.
|
||||||
22
stt/server/pyproject.toml
Normal file
22
stt/server/pyproject.toml
Normal file
|
|
@ -0,0 +1,22 @@
|
||||||
|
[project]
|
||||||
|
name = "stt-server"
|
||||||
|
version = "0.1.0"
|
||||||
|
description = "STT-server — orchestrateur AI du homelab Funk (API pour les clients STT)"
|
||||||
|
requires-python = ">=3.11"
|
||||||
|
|
||||||
|
dependencies = [
|
||||||
|
"fastapi>=0.110",
|
||||||
|
"uvicorn[standard]>=0.29",
|
||||||
|
"httpx>=0.27",
|
||||||
|
]
|
||||||
|
|
||||||
|
[project.scripts]
|
||||||
|
stt-server = "stt_server.app:run"
|
||||||
|
|
||||||
|
[build-system]
|
||||||
|
requires = ["setuptools>=68"]
|
||||||
|
build-backend = "setuptools.build_meta"
|
||||||
|
|
||||||
|
[tool.setuptools.packages.find]
|
||||||
|
where = ["."]
|
||||||
|
include = ["stt_server*"]
|
||||||
3
stt/server/stt_server/__init__.py
Normal file
3
stt/server/stt_server/__init__.py
Normal file
|
|
@ -0,0 +1,3 @@
|
||||||
|
"""STT-server — orchestrateur AI in-cluster pour les clients STT."""
|
||||||
|
|
||||||
|
__version__ = "0.1.0"
|
||||||
49
stt/server/stt_server/app.py
Normal file
49
stt/server/stt_server/app.py
Normal file
|
|
@ -0,0 +1,49 @@
|
||||||
|
"""STT-server — API FastAPI pour les clients STT.
|
||||||
|
|
||||||
|
Endpoints :
|
||||||
|
GET /healthz → état du service
|
||||||
|
POST /v1/ask {text} → {reply} (requête AI, orchestrée vers LiteLLM)
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import httpx
|
||||||
|
from fastapi import FastAPI, HTTPException
|
||||||
|
from pydantic import BaseModel
|
||||||
|
|
||||||
|
from stt_server import __version__
|
||||||
|
from stt_server.brain import ask as brain_ask
|
||||||
|
|
||||||
|
app = FastAPI(title="STT-server", version=__version__)
|
||||||
|
|
||||||
|
|
||||||
|
class AskRequest(BaseModel):
|
||||||
|
text: str
|
||||||
|
|
||||||
|
|
||||||
|
class AskReply(BaseModel):
|
||||||
|
reply: str
|
||||||
|
|
||||||
|
|
||||||
|
@app.get("/healthz")
|
||||||
|
async def healthz() -> dict:
|
||||||
|
return {"status": "ok", "version": __version__}
|
||||||
|
|
||||||
|
|
||||||
|
@app.post("/v1/ask", response_model=AskReply)
|
||||||
|
async def v1_ask(req: AskRequest) -> AskReply:
|
||||||
|
text = req.text.strip()
|
||||||
|
if not text:
|
||||||
|
raise HTTPException(status_code=400, detail="text vide")
|
||||||
|
try:
|
||||||
|
reply = await brain_ask(text)
|
||||||
|
except httpx.HTTPError as e:
|
||||||
|
raise HTTPException(status_code=502, detail=f"upstream LiteLLM : {e}") from e
|
||||||
|
return AskReply(reply=reply)
|
||||||
|
|
||||||
|
|
||||||
|
def run() -> None:
|
||||||
|
"""Entrypoint `stt-server` (dev local). En prod : uvicorn via le conteneur."""
|
||||||
|
import uvicorn
|
||||||
|
|
||||||
|
uvicorn.run(app, host="0.0.0.0", port=8000) # noqa: S104 — service interne au cluster
|
||||||
29
stt/server/stt_server/brain.py
Normal file
29
stt/server/stt_server/brain.py
Normal file
|
|
@ -0,0 +1,29 @@
|
||||||
|
"""Orchestration AI : route les requêtes des clients vers LiteLLM (s01).
|
||||||
|
|
||||||
|
LiteLLM (:4000) est OpenAI-compatible et route lui-même vers Qwen3 (g01) ou Claude
|
||||||
|
selon l'alias `hermes-default` / `hermes-switch`. L'intégration des outils Hermes
|
||||||
|
(« agir sur Funk » via le gateway :8080) est une étape ultérieure.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import httpx
|
||||||
|
|
||||||
|
from stt_server.config import settings
|
||||||
|
|
||||||
|
|
||||||
|
async def ask(text: str) -> str:
|
||||||
|
payload = {
|
||||||
|
"model": settings.model,
|
||||||
|
"messages": [
|
||||||
|
{"role": "system", "content": settings.system_prompt},
|
||||||
|
{"role": "user", "content": text},
|
||||||
|
],
|
||||||
|
"max_tokens": settings.max_tokens,
|
||||||
|
"temperature": settings.temperature,
|
||||||
|
}
|
||||||
|
headers = {"Authorization": f"Bearer {settings.litellm_key}"}
|
||||||
|
async with httpx.AsyncClient(timeout=settings.request_timeout) as client:
|
||||||
|
r = await client.post(settings.litellm_url, json=payload, headers=headers)
|
||||||
|
r.raise_for_status()
|
||||||
|
return r.json()["choices"][0]["message"]["content"].strip()
|
||||||
27
stt/server/stt_server/config.py
Normal file
27
stt/server/stt_server/config.py
Normal file
|
|
@ -0,0 +1,27 @@
|
||||||
|
"""Configuration du STT-server — via variables d'environnement (12-factor / k8s)."""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import os
|
||||||
|
|
||||||
|
|
||||||
|
class Settings:
|
||||||
|
# LiteLLM (s01) — joint via un Service ExternalName/Endpoints dans le cluster.
|
||||||
|
litellm_url: str = os.getenv(
|
||||||
|
"STT_LITELLM_URL", "http://litellm-ext:4000/v1/chat/completions"
|
||||||
|
)
|
||||||
|
litellm_key: str = os.getenv("STT_LITELLM_KEY", "lm-studio")
|
||||||
|
model: str = os.getenv("STT_MODEL", "hermes-default")
|
||||||
|
|
||||||
|
system_prompt: str = os.getenv(
|
||||||
|
"STT_SYSTEM_PROMPT",
|
||||||
|
"Tu es Hermes, l'assistant vocal du homelab Funk. "
|
||||||
|
"Réponds toujours en français, de façon concise (2-3 phrases maximum), "
|
||||||
|
"sans markdown ni listes.",
|
||||||
|
)
|
||||||
|
max_tokens: int = int(os.getenv("STT_MAX_TOKENS", "200"))
|
||||||
|
temperature: float = float(os.getenv("STT_TEMPERATURE", "0.7"))
|
||||||
|
request_timeout: float = float(os.getenv("STT_REQUEST_TIMEOUT", "60"))
|
||||||
|
|
||||||
|
|
||||||
|
settings = Settings()
|
||||||
Loading…
Add table
Add a link
Reference in a new issue