feat(voxtral): STT-Satellit (Profil) + 2-Karten-GPU-Pinning
Neuer xtts/voxtral-Container (RVS<->vLLM-Realtime-WS, adaptiver Endpointer), hinter Compose-Profil 'voxtral' (braucht >=16GB VRAM, startet nicht im Default). Bestehende Satelliten auf die zwei 3060 gepinnt: whisper->GPU0, f5tts->GPU1, llama-swap->GPU0. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Claude Opus 4.8
+56
-3
@@ -30,7 +30,7 @@ services:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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device_ids: ["1"] # TTS-Rolle → GPU 1 (spaeter: Voxtral-TTS)
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capabilities: [gpu]
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volumes:
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- ./voices:/voices # WAV + TXT Referenz
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@@ -68,7 +68,7 @@ services:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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device_ids: ["0"] # STT-Rolle → GPU 0 (spaeter: Voxtral-STT-3B)
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capabilities: [gpu]
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environment:
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- RVS_HOST=${RVS_HOST}
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@@ -108,7 +108,7 @@ services:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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device_ids: ["0"] # LLM → GPU 0 (teilt sich mit dem kleinen STT)
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capabilities: [gpu]
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volumes:
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- ./models:/models # HF-Download-Cache (persistent)
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@@ -137,3 +137,56 @@ services:
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# Erster Load eines Modells kann ein GGUF ziehen (mehrere GB) — grosszuegig.
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- LLM_TIMEOUT_SEC=${LLM_TIMEOUT_SEC:-600}
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restart: unless-stopped
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# ─── Voxtral STT (GPU, Realtime) — PROFIL "voxtral" ───────────
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# Ersetzt whisper als STT sobald die 24-GB-Karte da ist. Startet NUR mit
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# docker compose --profile voxtral up -d
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# (sonst kollidiert es mit whisper — beide wuerden stt_* beantworten).
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#
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# ⚠️ VRAM: Voxtral-Mini-4B-Realtime-2602 braucht >=16 GB (BF16, laut vLLM-
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# Rezept keine Quant). Laeuft NICHT auf der 3060 (12 GB) — erst 24-GB-Karte.
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# ⚠️ vLLM: Version >=0.20.0 noetig. Entrypoint/Serve-Form beim ersten Lauf
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# gegen das offizielle Rezept pruefen (siehe voxtral/README.md).
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voxtral-vllm:
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image: vllm/vllm-openai:latest
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container_name: aria-voxtral-vllm
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profiles: ["voxtral"]
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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volumes:
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- ./hf-cache:/root/.cache/huggingface # gleicher Modell-Cache wie whisper/f5
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environment:
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- VLLM_DISABLE_COMPILE_CACHE=1
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- HUGGING_FACE_HUB_TOKEN=${HF_TOKEN:-}
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# Serve-Command aus dem vLLM-Rezept (Voxtral-Mini-4B-Realtime-2602).
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command:
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- --model
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- mistralai/Voxtral-Mini-4B-Realtime-2602
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- --tokenizer-mode
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- mistral
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- --compilation_config
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- '{"cudagraph_mode":"PIECEWISE"}'
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restart: unless-stopped
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# ─── Voxtral-Bridge — RVS <-> vLLM-Realtime-WS (CPU-Glue) ─────
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voxtral-bridge:
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build: ./voxtral
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container_name: aria-voxtral-bridge
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profiles: ["voxtral"]
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depends_on:
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- voxtral-vllm
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environment:
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- RVS_HOST=${RVS_HOST}
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- RVS_PORT=${RVS_PORT:-443}
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- RVS_TLS=${RVS_TLS:-true}
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- RVS_TLS_FALLBACK=${RVS_TLS_FALLBACK:-true}
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- RVS_TOKEN=${RVS_TOKEN}
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- VOXTRAL_VLLM_URL=ws://voxtral-vllm:8000/v1/realtime
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- VOXTRAL_MODEL=mistralai/Voxtral-Mini-4B-Realtime-2602
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- VOXTRAL_LANGUAGE=${WHISPER_LANGUAGE:-de}
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restart: unless-stopped
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@@ -0,0 +1,14 @@
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# Voxtral-BRIDGE (nicht das Modell!) — leichte CPU-Glue zwischen RVS und dem
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# vLLM-Realtime-Server. Das eigentliche Voxtral-Modell laeuft im Container
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# `voxtral-vllm` (GPU, vllm/vllm-openai). Deshalb hier kein CUDA-Base noetig.
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FROM python:3.11-slim
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ENV PYTHONUNBUFFERED=1
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY bridge.py ./
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CMD ["python3", "bridge.py"]
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@@ -0,0 +1,70 @@
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# Voxtral-STT-Satellit (M0.3)
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Streaming-STT via **Voxtral-Mini-4B-Realtime-2602** (Mistral, Apache 2.0) auf
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vLLM. Ersetzt whisper als STT — genauer (~5,9 % WER vs 7,4 % FLEURS) und mit
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echtem Realtime-Streaming. Deutsch ist in den 13 Sprachen abgedeckt.
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Zwei Container:
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- **`voxtral-vllm`** — das Modell auf vLLM (GPU). Exponiert die Realtime-WS-API.
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- **`voxtral-bridge`** — CPU-Glue: RVS ⇄ vLLM-Realtime-WS. Macht das Endpointing
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selbst (adaptiver Rausch-Boden-VAD, identisch zur whisper-Bridge / M0.1).
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## ⚠️ Hardware-Realität — läuft NICHT auf der 3060
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Das Realtime-Modell braucht laut [vLLM-Rezept](https://recipes.vllm.ai/mistralai/Voxtral-Mini-4B-Realtime-2602)
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**≥ 16 GB VRAM (BF16, keine Quant)**. Die RTX 3060 hat 12 GB → passt nicht.
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- **Interim-gpubox (nur 3060):** whisper (mit M0.1-Fix) + F5-TTS bleiben aktiv.
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Voxtral NICHT starten.
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- **Ab der 24-GB-Karte:** Voxtral-Profil hochziehen, whisper wird Fallback.
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Deshalb liegen beide Services hinter dem Compose-**Profil `voxtral`** und starten
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NUR explizit — sonst würden whisper *und* voxtral dieselben `stt_*`-Messages
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beantworten (Kollision).
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## Starten (erst wenn die 24-GB-Karte drin ist)
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```bash
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cd xtts
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docker compose --profile voxtral up -d --build
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docker logs -f aria-voxtral-vllm # laedt Modell (mehrere GB, dauert)
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docker logs -f aria-voxtral-bridge # "RVS verbunden" + service_status ready
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```
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Whisper vorher stoppen, damit nur eine STT-Engine antwortet:
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```bash
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docker compose stop whisper-bridge
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```
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## ⚠️ Auf echter Hardware verifizieren (blind gebaut, kein Test hier)
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1. **vLLM-Version ≥ 0.20.0** und die **Serve-Form**. Das Rezept nutzt
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`vllm serve <model> …`. Falls das `vllm/vllm-openai`-Image einen anderen
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Entrypoint hat, das `command:` in `docker-compose.yml` anpassen
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(Rezept-Command steht dort als Kommentar).
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2. **Realtime-WS-Frames.** Die exakten Event-Namen sind in `bridge.py` ganz oben
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als Konstanten gebündelt (`VLLM_SEND_APPEND`, `VLLM_DELTA_SUFFIXES`, …),
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modelliert nach dem OpenAI-Realtime-Schema. Gegen das offizielle
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**vLLM-Realtime-Client-Beispiel** prüfen und dort anpassen — nur an dieser
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einen Stelle. Das Response-Handling ist bereits defensiv (mehrere Feldnamen).
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3. **Endpoint-URL/Port.** Default `ws://voxtral-vllm:8000/v1/realtime` — prüfen ob
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vLLM auf 8000 lauscht und `/v1/realtime` registriert (Log-Zeile
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`Route: /v1/realtime`).
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4. **Endpointing.** Voxtral liefert keine eigene VAD → unser adaptiver Endpointer
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entscheidet (akustisch + semantisch am Delta-Wachstum). `endpointMs` kommt wie
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bei whisper aus der App; `voiceFactor` per Session tunebar.
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## Protokoll (RVS, identisch zu whisper — drop-in)
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Rein: `stt_stream_start`, `stt_audio_chunk` (16 kHz mono s16le, base64), `stt_stream_end`.
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Raus: `stt_partial`, `stt_endpoint` (das Event, auf das aria-bridge horcht), `stt_stream_done`.
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## TTS-Hinweis
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Voxtral-**TTS** (Voice-Cloning) ist hier NICHT enthalten — braucht ebenfalls
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16 GB VRAM und ist ein eigener Bau. Bis zur 24-GB-Karte bleibt **F5-TTS** aktiv.
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Danach: eigener `voxtral-tts`-Satellit (separates Ticket).
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## Quellen
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- Rezept: https://recipes.vllm.ai/mistralai/Voxtral-Mini-4B-Realtime-2602
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- vLLM Speech-to-Text: https://docs.vllm.ai/en/latest/serving/online_serving/speech_to_text/
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- Modell: https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602
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@@ -0,0 +1,494 @@
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#!/usr/bin/env python3
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"""
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ARIA Voxtral Bridge — Streaming-STT via Voxtral-Mini-4B-Realtime-2602 (vLLM).
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Zwilling der whisper-Bridge, aber das Transkribieren macht NICHT faster-whisper
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im selben Prozess, sondern der separate vLLM-Realtime-Server (Container
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`voxtral-vllm`) ueber dessen WebSocket-API `/v1/realtime`. Diese Bridge ist
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reine Glue:
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App ──(RVS: stt_stream_start / stt_audio_chunk / stt_stream_end)──▶ diese Bridge
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diese Bridge ──(WS /v1/realtime: PCM16-b64 append)──▶ voxtral-vllm
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voxtral-vllm ──(transcription.delta / transcription.done)──▶ diese Bridge
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diese Bridge ──(RVS: stt_partial / stt_endpoint / stt_stream_done)──▶ App/aria-bridge
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Das RVS-Wire-Protokoll ist IDENTISCH zur whisper-Bridge (drop-in). Das
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Endpointing (wann hat der User aufgehoert zu sprechen) macht diese Bridge
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selbst — mit demselben ADAPTIVEN Rausch-Boden-Endpointer wie whisper (Voxtral
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Realtime liefert laut vLLM-Doku keine eigene VAD/„speaker done"-Semantik, nur
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transcription.delta/.done). Die akustische Energie messen wir auf unserer
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eigenen PCM-Kopie, die semantische Stagnation am Delta-Textwachstum.
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⚠️ HARDWARE: Voxtral-Mini-4B-Realtime-2602 braucht >=16 GB VRAM (BF16). Auf der
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RTX 3060 (12 GB) laeuft es NICHT — erst auf der 24-GB-Karte. Bis dahin
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bleibt die whisper-Bridge aktiv (Profil-gesteuert im docker-compose).
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⚠️ VERIFY-ON-FIRST-RUN: Die exakten vLLM-Realtime-FRAME-Namen (Audio-Append,
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Delta/Done-Event-Typen) sind unten als Konstanten gebuendelt und nach dem
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OpenAI-Realtime-Schema modelliert. Gegen das offizielle vLLM-Realtime-
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Client-Beispiel pruefen und ggf. anpassen — sie stehen bewusst an EINER
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Stelle. Response-Handling ist defensiv (mehrere moegliche Feldnamen).
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Env:
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RVS_HOST, RVS_PORT, RVS_TLS, RVS_TLS_FALLBACK, RVS_TOKEN
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VOXTRAL_VLLM_URL Default: ws://voxtral-vllm:8000/v1/realtime
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VOXTRAL_MODEL Default: mistralai/Voxtral-Mini-4B-Realtime-2602
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VOXTRAL_LANGUAGE Default: de
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"""
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import asyncio
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import base64
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import json
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import logging
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import os
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import time
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from dataclasses import dataclass, field
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from typing import Optional
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import numpy as np
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import websockets
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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datefmt="%H:%M:%S",
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)
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logger = logging.getLogger("voxtral-bridge")
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RVS_HOST = os.getenv("RVS_HOST", "").strip()
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RVS_PORT = int(os.getenv("RVS_PORT", "443"))
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RVS_TLS = os.getenv("RVS_TLS", "true").lower() == "true"
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RVS_TLS_FALLBACK = os.getenv("RVS_TLS_FALLBACK", "true").lower() == "true"
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RVS_TOKEN = os.getenv("RVS_TOKEN", "").strip()
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VOXTRAL_VLLM_URL = os.getenv("VOXTRAL_VLLM_URL", "ws://voxtral-vllm:8000/v1/realtime")
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VOXTRAL_MODEL = os.getenv("VOXTRAL_MODEL", "mistralai/Voxtral-Mini-4B-Realtime-2602")
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VOXTRAL_LANGUAGE = os.getenv("VOXTRAL_LANGUAGE", "de")
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# ── vLLM-Realtime-Frames — HIER anpassen falls das Client-Beispiel abweicht ──
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# Senderichtung (wir → vLLM): PCM16-16kHz-mono base64 anhaengen + committen.
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VLLM_SEND_APPEND = "input_audio_buffer.append" # {"type":..., "audio": "<b64>"}
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VLLM_SEND_COMMIT = "input_audio_buffer.commit" # Buffer abschliessen
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VLLM_AUDIO_FIELD = "audio"
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# Empfangsrichtung (vLLM → wir): inkrementeller Text + final. Defensiv geprueft.
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VLLM_DELTA_SUFFIXES = ("transcription.delta",) # msg["type"] endet hierauf
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VLLM_DONE_SUFFIXES = ("transcription.done", "transcription.completed")
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VLLM_DELTA_FIELDS = ("delta", "text", "transcription") # eins davon traegt den Text
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# ── Streaming-/Endpointing-Parameter (analog whisper-Bridge) ──
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STREAM_DEFAULT_ENDPOINT_MS = 2400
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STREAM_DEFAULT_HARD_CAP_MS = 60000
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STREAM_MIN_AUDIO_MS = 600
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STREAM_SESSION_TTL_S = 120
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STREAM_ENERGY_WINDOW_MS = 300
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STREAM_SEMANTIC_BACKUP_FACTOR = 2.0
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# Adaptiver Voice-Schwellwert (siehe whisper-Bridge M0.1): Grenze relativ zum
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# gemessenen Rausch-Boden statt fix — schneidet leises Sprechen nicht ab.
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STREAM_VOICE_FACTOR = 2.5
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STREAM_VOICE_RMS_MIN = 0.005
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STREAM_VOICE_RMS_MAX = 0.020
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def pcm_s16le_to_float32(data: bytes) -> np.ndarray:
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if not data:
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return np.zeros(0, dtype=np.float32)
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arr = np.frombuffer(data, dtype=np.int16).astype(np.float32) / 32768.0
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return arr
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async def _send(ws, mtype: str, payload: dict) -> None:
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try:
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await ws.send(json.dumps({
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"type": mtype,
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"payload": payload,
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"timestamp": int(time.time() * 1000),
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}))
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except Exception as e:
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logger.warning("RVS-Send fehlgeschlagen (%s): %s", mtype, e)
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@dataclass
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class StreamSession:
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request_id: str
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audio_request_id: str
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language: str
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endpoint_ms: int
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hard_cap_ms: int
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voice: str = ""
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speed: float = 1.0
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interrupted: bool = False
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location: Optional[dict] = None
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sample_rate: int = 16000
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voice_factor: float = STREAM_VOICE_FACTOR
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voice_rms_min: float = STREAM_VOICE_RMS_MIN
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voice_rms_max: float = STREAM_VOICE_RMS_MAX
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pcm_buffer: bytearray = field(default_factory=bytearray)
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started_at: float = field(default_factory=time.time)
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last_chunk_at: float = field(default_factory=time.time)
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last_partial: str = ""
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last_growth_at: float = 0.0
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last_voice_at: float = 0.0
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noise_floor: float = 0.0
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closed: bool = False
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endpoint_sent: bool = False
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# vLLM-Realtime-Session
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vllm_ws: object = None
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vllm_reader: object = None
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class SessionManager:
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def __init__(self) -> None:
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self._sessions: dict[str, StreamSession] = {}
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self._ws = None # RVS
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def attach_ws(self, ws) -> None:
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self._ws = ws
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async def start_session(self, payload: dict) -> Optional[StreamSession]:
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request_id = (payload.get("requestId") or "").strip()
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if not request_id:
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logger.warning("stt_stream_start ohne requestId — ignoriert")
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return None
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try:
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endpoint_ms = int(payload.get("endpointMs") or STREAM_DEFAULT_ENDPOINT_MS)
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except (TypeError, ValueError):
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endpoint_ms = STREAM_DEFAULT_ENDPOINT_MS
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try:
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hard_cap_ms = int(payload.get("hardCapMs") or STREAM_DEFAULT_HARD_CAP_MS)
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except (TypeError, ValueError):
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hard_cap_ms = STREAM_DEFAULT_HARD_CAP_MS
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try:
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voice_factor = float(payload.get("voiceFactor") or STREAM_VOICE_FACTOR)
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except (TypeError, ValueError):
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voice_factor = STREAM_VOICE_FACTOR
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sess = StreamSession(
|
||||
request_id=request_id,
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audio_request_id=payload.get("audioRequestId", "") or "",
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language=payload.get("language") or VOXTRAL_LANGUAGE,
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endpoint_ms=endpoint_ms,
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hard_cap_ms=hard_cap_ms,
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voice=payload.get("voice", "") or "",
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speed=float(payload.get("speed") or 1.0),
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voice_factor=voice_factor,
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interrupted=bool(payload.get("interrupted", False)),
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location=payload.get("location") or None,
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sample_rate=int(payload.get("sampleRate") or 16000),
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)
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# vLLM-Realtime-Session oeffnen + Reader starten.
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try:
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sess.vllm_ws = await websockets.connect(VOXTRAL_VLLM_URL, max_size=8 * 1024 * 1024)
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await self._vllm_configure(sess)
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sess.vllm_reader = asyncio.create_task(self._vllm_read_loop(sess))
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||||
except Exception as e:
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||||
logger.exception("Stream %s: vLLM-Realtime-Connect fehlgeschlagen: %s",
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||||
request_id[:8], e)
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||||
# ohne Backend keine Transkription → sofort leeres Endpoint melden
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||||
self._sessions[request_id] = sess
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await self._finalize(sess, reason="vllm_unavailable")
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||||
return None
|
||||
self._sessions[request_id] = sess
|
||||
logger.info("Voxtral-Session offen: id=%s lang=%s endpointMs=%d",
|
||||
request_id[:8], sess.language, sess.endpoint_ms)
|
||||
return sess
|
||||
|
||||
async def _vllm_configure(self, sess: StreamSession) -> None:
|
||||
"""Optionale Session-Konfig an vLLM (Modell/Sprache/temperature=0).
|
||||
VERIFY: exaktes session.update-Schema gegen vLLM-Realtime-Beispiel.
|
||||
Best-effort — Fehler hier sind nicht fatal."""
|
||||
try:
|
||||
await sess.vllm_ws.send(json.dumps({
|
||||
"type": "session.update",
|
||||
"session": {
|
||||
"model": VOXTRAL_MODEL,
|
||||
"language": sess.language,
|
||||
"temperature": 0.0,
|
||||
"input_audio_format": "pcm16",
|
||||
},
|
||||
}))
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
async def _vllm_read_loop(self, sess: StreamSession) -> None:
|
||||
"""Liest transcription.delta/.done vom vLLM-Realtime-Server."""
|
||||
ws = sess.vllm_ws
|
||||
try:
|
||||
async for raw in ws:
|
||||
try:
|
||||
msg = json.loads(raw)
|
||||
except Exception:
|
||||
continue
|
||||
mtype = str(msg.get("type", ""))
|
||||
if any(mtype.endswith(s) for s in VLLM_DELTA_SUFFIXES):
|
||||
text = self._extract_text(msg)
|
||||
if text:
|
||||
await self._on_delta(sess, text)
|
||||
elif any(mtype.endswith(s) for s in VLLM_DONE_SUFFIXES):
|
||||
text = self._extract_text(msg)
|
||||
if text:
|
||||
await self._on_delta(sess, text, final=True)
|
||||
except Exception:
|
||||
logger.debug("Stream %s: vLLM-Reader beendet", sess.request_id[:8])
|
||||
|
||||
@staticmethod
|
||||
def _extract_text(msg: dict) -> str:
|
||||
for f in VLLM_DELTA_FIELDS:
|
||||
v = msg.get(f)
|
||||
if isinstance(v, str) and v:
|
||||
return v
|
||||
return ""
|
||||
|
||||
async def _on_delta(self, sess: StreamSession, text: str, final: bool = False) -> None:
|
||||
"""Neuer/finaler Transkript-Text vom vLLM. delta = inkrementell; wir
|
||||
haengen an, wenn er den bisherigen Partial verlaengert, sonst ersetzen
|
||||
wir (Voxtral kann korrigieren)."""
|
||||
if final or text.startswith(sess.last_partial):
|
||||
new_full = text if final else text
|
||||
else:
|
||||
new_full = (sess.last_partial + text).strip()
|
||||
new_full = new_full.strip()
|
||||
if new_full and new_full != sess.last_partial:
|
||||
sess.last_partial = new_full
|
||||
sess.last_growth_at = time.time()
|
||||
if self._ws is not None:
|
||||
await _send(self._ws, "stt_partial", {
|
||||
"requestId": sess.request_id,
|
||||
"audioRequestId": sess.audio_request_id,
|
||||
"text": new_full,
|
||||
})
|
||||
|
||||
def feed_chunk(self, payload: dict) -> bool:
|
||||
request_id = payload.get("requestId", "")
|
||||
sess = self._sessions.get(request_id)
|
||||
if sess is None or sess.closed:
|
||||
return False
|
||||
pcm_b64 = payload.get("pcm", "")
|
||||
if not pcm_b64:
|
||||
return True
|
||||
try:
|
||||
pcm = base64.b64decode(pcm_b64)
|
||||
except Exception:
|
||||
return True
|
||||
sess.pcm_buffer.extend(pcm)
|
||||
sess.last_chunk_at = time.time()
|
||||
# An vLLM weiterreichen (fire-and-forget).
|
||||
if sess.vllm_ws is not None:
|
||||
asyncio.create_task(self._vllm_append(sess, pcm_b64))
|
||||
return True
|
||||
|
||||
async def _vllm_append(self, sess: StreamSession, pcm_b64: str) -> None:
|
||||
try:
|
||||
await sess.vllm_ws.send(json.dumps({
|
||||
"type": VLLM_SEND_APPEND,
|
||||
VLLM_AUDIO_FIELD: pcm_b64,
|
||||
}))
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def end_session(self, request_id: str) -> None:
|
||||
sess = self._sessions.get(request_id)
|
||||
if sess is not None:
|
||||
sess.closed = True
|
||||
|
||||
def drop(self, request_id: str) -> None:
|
||||
sess = self._sessions.pop(request_id, None)
|
||||
if sess is not None:
|
||||
self._teardown_vllm(sess)
|
||||
|
||||
def _teardown_vllm(self, sess: StreamSession) -> None:
|
||||
try:
|
||||
if sess.vllm_reader is not None:
|
||||
sess.vllm_reader.cancel()
|
||||
except Exception:
|
||||
pass
|
||||
if sess.vllm_ws is not None:
|
||||
asyncio.create_task(self._close_ws(sess.vllm_ws))
|
||||
sess.vllm_ws = None
|
||||
|
||||
@staticmethod
|
||||
async def _close_ws(ws) -> None:
|
||||
try:
|
||||
await ws.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# ── Endpointer (adaptiv, wie whisper-Bridge M0.1) ──
|
||||
def _buffer_duration_ms(self, sess: StreamSession) -> float:
|
||||
samples = len(sess.pcm_buffer) // 2
|
||||
return (samples / sess.sample_rate) * 1000.0 if samples else 0.0
|
||||
|
||||
def _tail_rms(self, sess: StreamSession) -> float:
|
||||
win_bytes = int(sess.sample_rate * STREAM_ENERGY_WINDOW_MS / 1000) * 2
|
||||
if win_bytes <= 0:
|
||||
return 0.0
|
||||
tail = sess.pcm_buffer[-win_bytes:]
|
||||
if len(tail) < 2:
|
||||
return 0.0
|
||||
arr = pcm_s16le_to_float32(bytes(tail))
|
||||
if arr.size == 0:
|
||||
return 0.0
|
||||
return float(np.sqrt(np.mean(arr * arr)))
|
||||
|
||||
def _voice_threshold(self, sess: StreamSession) -> float:
|
||||
nf = sess.noise_floor
|
||||
if nf <= 0.0:
|
||||
return sess.voice_rms_min
|
||||
return min(max(nf * sess.voice_factor, sess.voice_rms_min), sess.voice_rms_max)
|
||||
|
||||
def _update_noise_floor(self, sess: StreamSession, rms: float) -> None:
|
||||
nf = sess.noise_floor
|
||||
if nf <= 0.0:
|
||||
sess.noise_floor = rms
|
||||
elif rms < nf:
|
||||
sess.noise_floor = 0.90 * nf + 0.10 * rms
|
||||
else:
|
||||
sess.noise_floor = 0.98 * nf + 0.02 * rms
|
||||
|
||||
async def run_endpointer(self) -> None:
|
||||
logger.info("Voxtral-Endpointer gestartet (adaptiver VAD)")
|
||||
while True:
|
||||
await asyncio.sleep(0.2)
|
||||
now = time.time()
|
||||
for sid, sess in list(self._sessions.items()):
|
||||
try:
|
||||
await self._tick(sess, now)
|
||||
except Exception:
|
||||
logger.exception("Endpointer-Tick crashed (session=%s)", sid[:8])
|
||||
for sid, sess in list(self._sessions.items()):
|
||||
if now - sess.last_chunk_at > STREAM_SESSION_TTL_S:
|
||||
logger.info("Stream %s: TTL — drop", sid[:8])
|
||||
self.drop(sid)
|
||||
|
||||
async def _tick(self, sess: StreamSession, now: float) -> None:
|
||||
if sess.endpoint_sent:
|
||||
return
|
||||
elapsed_ms = (now - sess.started_at) * 1000.0
|
||||
if elapsed_ms > sess.hard_cap_ms and not sess.closed:
|
||||
await self._finalize(sess, reason="hardcap")
|
||||
return
|
||||
if sess.closed:
|
||||
await self._finalize(sess, reason="stream_end")
|
||||
return
|
||||
if self._buffer_duration_ms(sess) < STREAM_MIN_AUDIO_MS:
|
||||
return
|
||||
# adaptive akustische Sprach-Aktivitaet
|
||||
rms = self._tail_rms(sess)
|
||||
if rms >= self._voice_threshold(sess):
|
||||
sess.last_voice_at = now
|
||||
else:
|
||||
self._update_noise_floor(sess, rms)
|
||||
# Endpoint: akustisch (Primaer) oder semantisch (Backstop), sobald Text da
|
||||
if sess.last_growth_at > 0.0:
|
||||
acoustic_silence_ms = (now - sess.last_voice_at) * 1000.0 if sess.last_voice_at > 0 else 0.0
|
||||
semantic_silence_ms = (now - sess.last_growth_at) * 1000.0
|
||||
acoustic_done = sess.last_voice_at > 0 and acoustic_silence_ms >= sess.endpoint_ms
|
||||
semantic_done = semantic_silence_ms >= sess.endpoint_ms * STREAM_SEMANTIC_BACKUP_FACTOR
|
||||
if acoustic_done or semantic_done:
|
||||
await self._finalize(sess, reason="endpoint" if acoustic_done else "endpoint_semantic")
|
||||
|
||||
async def _finalize(self, sess: StreamSession, reason: str) -> None:
|
||||
if sess.endpoint_sent:
|
||||
return
|
||||
sess.endpoint_sent = True
|
||||
# vLLM ggf. committen, damit ein letztes transcription.done kommt.
|
||||
if sess.vllm_ws is not None:
|
||||
try:
|
||||
await sess.vllm_ws.send(json.dumps({"type": VLLM_SEND_COMMIT}))
|
||||
await asyncio.sleep(0.15) # kurz auf finalen Delta warten
|
||||
except Exception:
|
||||
pass
|
||||
final_text = sess.last_partial.strip()
|
||||
duration_s = self._buffer_duration_ms(sess) / 1000.0
|
||||
logger.info("Stream %s: FINAL (reason=%s, %.1fs): %r",
|
||||
sess.request_id[:8], reason, duration_s, final_text[:120])
|
||||
if self._ws is not None:
|
||||
endpoint_payload = {
|
||||
"requestId": sess.request_id,
|
||||
"audioRequestId": sess.audio_request_id,
|
||||
"text": final_text,
|
||||
"reason": reason,
|
||||
"durationS": duration_s,
|
||||
"sttMs": 0,
|
||||
"voice": sess.voice,
|
||||
"speed": sess.speed,
|
||||
"interrupted": sess.interrupted,
|
||||
}
|
||||
if sess.location:
|
||||
endpoint_payload["location"] = sess.location
|
||||
await _send(self._ws, "stt_endpoint", endpoint_payload)
|
||||
await _send(self._ws, "stt_stream_done", {
|
||||
"requestId": sess.request_id,
|
||||
"audioRequestId": sess.audio_request_id,
|
||||
"text": final_text,
|
||||
"reason": reason,
|
||||
})
|
||||
self.drop(sess.request_id)
|
||||
|
||||
|
||||
async def _broadcast_status(ws, state: str, **extra) -> None:
|
||||
payload = {"service": "voxtral", "state": state}
|
||||
payload.update(extra)
|
||||
await _send(ws, "service_status", payload)
|
||||
|
||||
|
||||
async def run_loop(sessions: SessionManager) -> None:
|
||||
use_tls = RVS_TLS
|
||||
retry_s = 2
|
||||
tls_fallback_tried = False
|
||||
while True:
|
||||
scheme = "wss" if use_tls else "ws"
|
||||
url = f"{scheme}://{RVS_HOST}:{RVS_PORT}/ws?token={RVS_TOKEN}"
|
||||
masked = url.replace(RVS_TOKEN, "***") if RVS_TOKEN else url
|
||||
try:
|
||||
logger.info("Verbinde zu RVS: %s", masked)
|
||||
async with websockets.connect(url, ping_interval=20, ping_timeout=10,
|
||||
max_size=50 * 1024 * 1024) as ws:
|
||||
logger.info("RVS verbunden")
|
||||
retry_s = 2
|
||||
tls_fallback_tried = False
|
||||
sessions.attach_ws(ws)
|
||||
await _broadcast_status(ws, "ready", model=VOXTRAL_MODEL)
|
||||
await _send(ws, "config_request", {"service": "voxtral"})
|
||||
|
||||
async for raw in ws:
|
||||
try:
|
||||
msg = json.loads(raw)
|
||||
except Exception:
|
||||
continue
|
||||
mtype = msg.get("type", "")
|
||||
payload = msg.get("payload", {}) or {}
|
||||
if mtype == "stt_stream_start":
|
||||
asyncio.create_task(sessions.start_session(payload))
|
||||
elif mtype == "stt_audio_chunk":
|
||||
sessions.feed_chunk(payload)
|
||||
elif mtype == "stt_stream_end":
|
||||
sessions.end_session(payload.get("requestId", ""))
|
||||
# stt_request (Legacy One-Shot) macht Voxtral hier NICHT —
|
||||
# dafuer bleibt die whisper-Bridge (Fallback).
|
||||
except Exception as e:
|
||||
logger.warning("RVS-Verbindung verloren: %s — retry in %ds", e, retry_s)
|
||||
if use_tls and RVS_TLS_FALLBACK and not tls_fallback_tried:
|
||||
use_tls = False
|
||||
tls_fallback_tried = True
|
||||
logger.info("TLS-Fallback: versuche ws:// (kein TLS)")
|
||||
continue
|
||||
await asyncio.sleep(retry_s)
|
||||
retry_s = min(retry_s * 2, 30)
|
||||
use_tls = RVS_TLS # fuer den naechsten Zyklus zuruecksetzen
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
if not RVS_HOST or not RVS_TOKEN:
|
||||
logger.error("RVS_HOST/RVS_TOKEN fehlen — .env pruefen. Abbruch.")
|
||||
return
|
||||
sessions = SessionManager()
|
||||
logger.info("Voxtral-Bridge startet — vLLM=%s Modell=%s", VOXTRAL_VLLM_URL, VOXTRAL_MODEL)
|
||||
await asyncio.gather(
|
||||
run_loop(sessions),
|
||||
sessions.run_endpointer(),
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
asyncio.run(main())
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
@@ -0,0 +1,5 @@
|
||||
# Voxtral-Bridge ist reine CPU-Glue (RVS-WS <-> vLLM-Realtime-WS). Das Modell
|
||||
# selbst laeuft im separaten voxtral-vllm-Container (GPU). Deshalb hier KEIN
|
||||
# torch/vllm — nur der WebSocket-Client + numpy fuer die RMS-Energiemessung.
|
||||
websockets>=12.0
|
||||
numpy>=1.24
|
||||
Reference in New Issue
Block a user