Add local Whisper speech recognition
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Mikei386
@@ -12,6 +12,7 @@ Bild- und Sprachausgabe. **Hermes und die Fach-MCPs laufen auf Unraid.**
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- Profile Router auf Port 8081
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- FLUX.2-klein-4B für Textbilder und Referenzbild-Bearbeitung auf der RTX 5080
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- XTTS auf der RTX 3060 mit Piper als CPU-Fallback
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- Whisper.cpp `large-v3-turbo` auf der CPU für lokale deutsche Spracherkennung
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- Live-Dashboard mit 21 Tagen Detailhistorie auf Port 8099
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- Portainer CE als optionale Container-Ansicht auf Port 9443
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- WireGuard-Gateway, Datenbackup und Athena-Operator
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@@ -98,6 +99,11 @@ Standard, bis Hermes' Sitzungsfehler behoben ist.
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- Router: `http://192.168.1.212:8081/v1`
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- Athena-Dashboard: `http://192.168.1.212:8099`
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Der Router stellt Sprache OpenAI-kompatibel bereit: Sprachausgabe über
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`/v1/audio/speech` und Spracherkennung über `/v1/audio/transcriptions`. Das
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Whisper-Modell liegt persistent im Docker-Volume `whisper-data`; Audiodaten
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werden lokal auf Athena verarbeitet.
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- Portainer: `https://192.168.1.212:9443`
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- Hermes-Dashboard auf Unraid: `http://192.168.1.2:9119`
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+39
-1
@@ -545,7 +545,9 @@ services:
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TTS_MODEL: piper
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TTS_VOICES: alloy
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TTS_DEFAULT_VOICE: alloy
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ENABLE_STT: "false"
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ENABLE_STT: "true"
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STT_WORKER_URL: http://whisper:8084
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STT_TIMEOUT: "300"
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networks: [frontend, control, inference]
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security_opt: ["no-new-privileges:true"]
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cap_drop: [ALL]
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@@ -569,6 +571,8 @@ services:
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condition: service_healthy
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tts-gateway:
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condition: service_healthy
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whisper:
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condition: service_healthy
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image-worker:
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build:
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@@ -710,6 +714,39 @@ services:
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retries: 12
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start_period: 10s
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whisper:
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build:
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context: .
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dockerfile: platform/docker/whisper/Dockerfile
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args:
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WHISPER_CPP_VERSION: ${WHISPER_CPP_VERSION:-v1.9.1}
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image: mike-ai/whisper:local
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container_name: mike-ai-whisper
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restart: unless-stopped
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read_only: true
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tmpfs:
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- /tmp:size=2g,mode=1777
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volumes:
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- whisper-data:/models
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environment:
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WHISPER_HOST: 0.0.0.0
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WHISPER_PORT: "8084"
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WHISPER_CLI: /opt/whisper.cpp/build/bin/whisper-cli
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WHISPER_MODEL: /models/ggml-large-v3-turbo.bin
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WHISPER_MODEL_URL: ${WHISPER_MODEL_URL:-https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v3-turbo.bin}
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WHISPER_THREADS: ${WHISPER_THREADS:-8}
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WHISPER_LANGUAGE: ${WHISPER_LANGUAGE:-de}
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networks: [frontend, inference]
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security_opt: ["no-new-privileges:true"]
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cap_drop: [ALL]
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cap_add: [CHOWN, SETUID, SETGID]
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healthcheck:
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test: [CMD, curl, -fsS, "http://127.0.0.1:8084/status"]
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interval: 10s
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timeout: 5s
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retries: 90
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start_period: 20m
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llama-dashboard:
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build: ./platform/llama-dashboard
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image: mike-ai/llama-dashboard:local
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@@ -809,6 +846,7 @@ networks:
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volumes:
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piper-data:
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whisper-data:
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router-state:
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router-images:
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portainer-data:
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@@ -8,6 +8,7 @@ flowchart LR
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P --> Q[genau ein llama.cpp-Profil<br/>Qwen Fast / Medium / Large / Ultra / Uncensored]
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R --> I[FLUX.2-klein-4B<br/>RTX 5080, Text + Editing]
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R --> T[XTTS RTX 3060<br/>Piper CPU-Fallback]
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R --> STT[Whisper.cpp large-v3-turbo<br/>CPU, lokale Spracherkennung]
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H --> U[MUA / Unraid MCP]
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H --> A[ARR-MCP]
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@@ -2,6 +2,19 @@
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Stand: 3. September 2026
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## Lokale Spracherkennung
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Athena betreibt Whisper.cpp v1.9.1 mit `large-v3-turbo` als CPU-Dienst. Der
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Profile Router veröffentlicht ihn als OpenAI-kompatiblen Endpunkt
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`/v1/audio/transcriptions`; Standardsprache ist Deutsch. Modell und Download
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bleiben im persistenten Docker-Volume `whisper-data` erhalten.
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Ein lokaler Rundlauftest (Athena-TTS → WAV → Athena-STT) wurde erfolgreich
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durchgeführt. OpenClaw ist ebenfalls auf diesen lokalen Endpunkt eingestellt
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und wurde mit `openclaw infer audio transcribe` erfolgreich geprüft. Für den
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lokalen Provider ist der Zugriff auf Athenas private IP ausdrücklich erlaubt;
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andere private Ziele werden dadurch nicht freigeschaltet.
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## Produktive llama.cpp-Runtime
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Alle Textprofile verwenden llama.cpp Build 10781,
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@@ -0,0 +1,35 @@
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FROM python:3.13.7-slim-bookworm
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ARG WHISPER_CPP_VERSION=v1.9.1
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RUN apt-get update \
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&& apt-get install -y --no-install-recommends \
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build-essential ca-certificates cmake curl ffmpeg git gosu \
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&& git clone --branch "${WHISPER_CPP_VERSION}" --depth 1 \
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https://github.com/ggml-org/whisper.cpp.git /opt/whisper.cpp \
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&& cmake -S /opt/whisper.cpp -B /opt/whisper.cpp/build \
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-DCMAKE_BUILD_TYPE=Release \
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-DGGML_NATIVE=ON \
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-DWHISPER_BUILD_TESTS=OFF \
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-DWHISPER_BUILD_SERVER=OFF \
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&& cmake --build /opt/whisper.cpp/build --config Release -j"$(nproc)" \
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&& apt-get purge -y --auto-remove build-essential cmake git \
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&& rm -rf /var/lib/apt/lists/* /root/.cache \
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&& useradd --system --uid 10004 --home-dir /nonexistent --shell /usr/sbin/nologin whisper
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WORKDIR /app
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COPY router/stt_worker.py /app/stt_worker.py
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COPY platform/docker/whisper/entrypoint.sh /usr/local/bin/mike-ai-whisper-entrypoint
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RUN chmod 0755 /usr/local/bin/mike-ai-whisper-entrypoint
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ENV WHISPER_HOST=0.0.0.0 \
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WHISPER_PORT=8084 \
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WHISPER_CLI=/opt/whisper.cpp/build/bin/whisper-cli \
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WHISPER_MODEL=/models/ggml-large-v3-turbo.bin \
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WHISPER_THREADS=8 \
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WHISPER_LANGUAGE=de \
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FFMPEG_BIN=/usr/bin/ffmpeg
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VOLUME ["/models"]
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EXPOSE 8084
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ENTRYPOINT ["/usr/local/bin/mike-ai-whisper-entrypoint"]
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@@ -0,0 +1,25 @@
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#!/bin/sh
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set -eu
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model=${WHISPER_MODEL:-/models/ggml-large-v3-turbo.bin}
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model_url=${WHISPER_MODEL_URL:-https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v3-turbo.bin}
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model_dir=$(dirname "$model")
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mkdir -p "$model_dir"
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chown 10004:10004 "$model_dir"
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if [ ! -s "$model" ]; then
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partial="${model}.part"
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echo "Downloading Whisper model to persistent storage"
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# All writes below must use the volume owner. The container deliberately
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# drops CAP_DAC_OVERRIDE, so even uid 0 cannot rename a file in the
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# whisper-owned directory after capabilities have been removed.
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if [ ! -s "$partial" ]; then
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gosu whisper rm -f "$partial"
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gosu whisper curl --fail --location --retry 5 --retry-delay 5 \
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--output "$partial" "$model_url"
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fi
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gosu whisper mv "$partial" "$model"
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fi
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exec gosu whisper python /app/stt_worker.py
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@@ -0,0 +1,70 @@
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#!/usr/bin/env python3
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"""End-to-end smoke test for Athena's OpenAI-compatible TTS and STT APIs."""
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import json
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import os
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import sys
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import urllib.request
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import uuid
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BASE_URL = os.environ.get("ROUTER_URL", "http://127.0.0.1:8081/v1").rstrip("/")
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API_KEY = os.environ.get("ROUTER_API_KEY", "")
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TEST_TEXT = "Dies ist ein lokaler Test der Spracherkennung auf Athena."
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def request(path: str, data: bytes, content_type: str) -> bytes:
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req = urllib.request.Request(
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f"{BASE_URL}{path}",
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data=data,
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headers={
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"Authorization": f"Bearer {API_KEY}",
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"Content-Type": content_type,
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},
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method="POST",
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)
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with urllib.request.urlopen(req, timeout=360) as response:
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return response.read()
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def main() -> int:
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if not API_KEY:
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print("ROUTER_API_KEY is required", file=sys.stderr)
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return 2
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speech = request(
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"/audio/speech",
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json.dumps({
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"model": "piper",
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"voice": "alloy",
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"response_format": "wav",
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"input": TEST_TEXT,
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}).encode(),
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"application/json",
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)
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boundary = f"speech-{uuid.uuid4().hex}"
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body = (
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f"--{boundary}\r\n"
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'Content-Disposition: form-data; name="model"\r\n\r\n'
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"whisper-1\r\n"
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f"--{boundary}\r\n"
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'Content-Disposition: form-data; name="language"\r\n\r\n'
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"de\r\n"
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f"--{boundary}\r\n"
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'Content-Disposition: form-data; name="file"; filename="test.wav"\r\n'
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"Content-Type: audio/wav\r\n\r\n"
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).encode() + speech + f"\r\n--{boundary}--\r\n".encode()
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result = json.loads(request(
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"/audio/transcriptions",
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body,
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f"multipart/form-data; boundary={boundary}",
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))
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transcript = result.get("text", "").strip()
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print(transcript)
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return 0 if transcript else 1
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if __name__ == "__main__":
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raise SystemExit(main())
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