feat: voice-agent MVP — LXC web frontend + Mac AI worker
LXC-Frontend (FastAPI + HTML/JS): - Audio-Upload (MP3/WAV/M4A/MP4/OGG/FLAC, max. 500 MB) - SQLite Job-Store, BackgroundTask-Pipeline - Job-Liste mit Live-Status, Downloads (DOCX + JSON) - Mac-Health-Indicator im UI Mac-Worker (FastAPI): - /api/transcribe (lightning-whisper-mlx | faster-whisper | mock) - /api/summarize + /api/protocol via Ollama (llama3.1:8b) - /api/export/docx via python-docx Deploy: - systemd-Service, Nginx Reverse-Proxy - deploy/install.sh: idempotentes LXC-Setup Doku: README.md, lxc-frontend/README.md, mac-worker/README.md
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from pathlib import Path
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
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app_host: str = "0.0.0.0"
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app_port: int = 8080
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whisper_engine: str = "mlx" # mlx | faster | mock
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whisper_model: str = "large-v3"
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whisper_batch_size: int = 12
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whisper_language: str = "de"
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ollama_url: str = "http://127.0.0.1:11434"
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ollama_model: str = "llama3.1:8b"
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ollama_timeout: int = 600
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work_dir: Path = Path("/tmp/voice-agent-mac")
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settings = Settings()
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settings.work_dir.mkdir(parents=True, exist_ok=True)
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from io import BytesIO
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from typing import Any
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from docx import Document
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from docx.shared import Pt
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def build_docx(protocol: dict[str, Any]) -> bytes:
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doc = Document()
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title = protocol.get("title") or "Sitzungsprotokoll"
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doc.add_heading(title, level=0)
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meta_lines = []
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if protocol.get("date"):
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meta_lines.append(f"Datum: {protocol['date']}")
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if protocol.get("topic"):
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meta_lines.append(f"Thema: {protocol['topic']}")
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if meta_lines:
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for line in meta_lines:
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doc.add_paragraph(line)
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participants = protocol.get("participants") or []
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if participants:
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doc.add_heading("Teilnehmer", level=1)
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for p in participants:
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doc.add_paragraph(str(p), style="List Bullet")
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summary = protocol.get("summary") or ""
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if summary:
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doc.add_heading("Zusammenfassung", level=1)
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doc.add_paragraph(summary)
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decisions = protocol.get("decisions") or []
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if decisions:
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doc.add_heading("Beschlüsse", level=1)
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for d in decisions:
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doc.add_paragraph(str(d), style="List Bullet")
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tasks = protocol.get("tasks") or []
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if tasks:
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doc.add_heading("Aufgaben", level=1)
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for t in tasks:
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if isinstance(t, dict):
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owner = t.get("owner") or "n/a"
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task = t.get("task") or t.get("description") or ""
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doc.add_paragraph(f"{owner}: {task}", style="List Bullet")
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else:
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doc.add_paragraph(str(t), style="List Bullet")
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questions = protocol.get("open_questions") or []
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if questions:
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doc.add_heading("Offene Fragen", level=1)
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for q in questions:
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doc.add_paragraph(str(q), style="List Bullet")
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next_meeting = protocol.get("next_meeting")
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if next_meeting:
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doc.add_heading("Nächster Termin", level=1)
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doc.add_paragraph(str(next_meeting))
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excerpt = protocol.get("transcript_excerpt") or []
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if excerpt:
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doc.add_heading("Transkript-Auszug", level=1)
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for item in excerpt:
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if isinstance(item, dict):
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time = item.get("time", "")
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speaker = item.get("speaker", "")
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text = item.get("text", "")
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p = doc.add_paragraph()
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run = p.add_run(f"[{time}] {speaker}: ")
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run.bold = True
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run.font.size = Pt(10)
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p.add_run(text).font.size = Pt(10)
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else:
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doc.add_paragraph(str(item))
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buf = BytesIO()
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doc.save(buf)
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return buf.getvalue()
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import json
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import logging
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import re
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from typing import Any
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import httpx
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from .config import settings
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log = logging.getLogger("mac-worker.ollama")
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SUMMARY_PROMPT = """Du bist ein präziser Protokollassistent für deutsche Geschäftssitzungen.
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Analysiere das folgende Sitzungstranskript und liefere ein JSON-Objekt mit:
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- "topic": Hauptthema der Sitzung (kurz)
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- "summary": Zusammenfassung in 3-6 Sätzen
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- "decisions": Liste der Beschlüsse (jeweils ein Satz)
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- "tasks": Liste der Aufgaben, jede mit "owner" (Name oder "n/a") und "task"
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- "participants": Liste der erkennbaren Teilnehmer (Namen oder "Sprecher 1/2/...")
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- "open_questions": Liste offener Fragen (kann leer sein)
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Antworte AUSSCHLIESSLICH mit gültigem JSON, keine Erläuterungen, kein Markdown-Codeblock.
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Sitzungstitel: {title}
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Transkript:
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\"\"\"
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{transcript}
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\"\"\"
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"""
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PROTOCOL_PROMPT = """Erstelle aus den folgenden Daten ein formelles deutsches Sitzungsprotokoll
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als JSON-Objekt mit folgenden Feldern:
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- "title": Protokoll-Titel
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- "date": Datum (YYYY-MM-DD, falls aus Transkript ableitbar, sonst leer)
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- "topic": Thema
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- "participants": Liste der Teilnehmer
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- "summary": Zusammenfassung
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- "decisions": Liste der Beschlüsse
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- "tasks": Liste der Aufgaben (jeweils mit "owner" und "task")
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- "open_questions": Liste offener Fragen
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- "next_meeting": Wenn erwähnt, sonst leer
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- "transcript_excerpt": Liste von Objekten mit "time", "speaker", "text" (max. 20 Einträge, zeitlich verteilt aus dem Transkript ziehen)
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Antworte AUSSCHLIESSLICH mit JSON, kein Markdown.
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Titel: {title}
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Bestehende Zusammenfassung: {summary_json}
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Transkript:
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\"\"\"
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{transcript}
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\"\"\"
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"""
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def _strip_codefence(s: str) -> str:
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s = s.strip()
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if s.startswith("```"):
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s = re.sub(r"^```(?:json)?", "", s, count=1).strip()
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if s.endswith("```"):
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s = s[:-3].strip()
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return s
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def _parse_json(text: str) -> dict:
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text = _strip_codefence(text)
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try:
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return json.loads(text)
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except json.JSONDecodeError:
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m = re.search(r"\{.*\}", text, re.S)
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if m:
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return json.loads(m.group(0))
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raise
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def _truncate(text: str, max_chars: int = 60000) -> str:
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if len(text) <= max_chars:
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return text
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head = text[: max_chars // 2]
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tail = text[-max_chars // 2 :]
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return head + "\n\n[... gekürzt ...]\n\n" + tail
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async def _chat(prompt: str) -> str:
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url = f"{settings.ollama_url.rstrip('/')}/api/generate"
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payload = {
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"model": settings.ollama_model,
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"prompt": prompt,
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"stream": False,
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"options": {"temperature": 0.2},
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}
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log.info("Ollama generate model=%s prompt_len=%d", settings.ollama_model, len(prompt))
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async with httpx.AsyncClient(timeout=settings.ollama_timeout) as c:
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r = await c.post(url, json=payload)
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r.raise_for_status()
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data = r.json()
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return data.get("response", "")
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async def summarize(transcript: str, title: str = "") -> dict[str, Any]:
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prompt = SUMMARY_PROMPT.format(title=title or "(ohne Titel)", transcript=_truncate(transcript))
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raw = await _chat(prompt)
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try:
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return _parse_json(raw)
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except Exception: # noqa: BLE001
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log.warning("Failed to parse summary JSON, falling back to text")
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return {
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"topic": title,
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"summary": raw.strip(),
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"decisions": [],
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"tasks": [],
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"participants": [],
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"open_questions": [],
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}
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async def make_protocol(transcript: str, summary: dict, title: str = "") -> dict:
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prompt = PROTOCOL_PROMPT.format(
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title=title or "(ohne Titel)",
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summary_json=json.dumps(summary, ensure_ascii=False),
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transcript=_truncate(transcript),
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)
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raw = await _chat(prompt)
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try:
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return _parse_json(raw)
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except Exception: # noqa: BLE001
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log.warning("Failed to parse protocol JSON, falling back to structured summary")
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return {
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"title": title,
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"date": "",
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"topic": summary.get("topic", title),
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"participants": summary.get("participants", []),
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"summary": summary.get("summary", ""),
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"decisions": summary.get("decisions", []),
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"tasks": summary.get("tasks", []),
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"open_questions": summary.get("open_questions", []),
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"next_meeting": "",
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"transcript_excerpt": [],
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}
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async def health() -> dict:
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url = f"{settings.ollama_url.rstrip('/')}/api/tags"
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async with httpx.AsyncClient(timeout=10) as c:
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r = await c.get(url)
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r.raise_for_status()
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return r.json()
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"""Abstraktion über mehrere Whisper-Implementierungen."""
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from __future__ import annotations
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import logging
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from pathlib import Path
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from typing import Any
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from .config import settings
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log = logging.getLogger("mac-worker.whisper")
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class WhisperEngine:
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def __init__(self) -> None:
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self.engine = settings.whisper_engine.lower()
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self._impl: Any = None
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def _ensure_loaded(self) -> None:
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if self._impl is not None:
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return
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if self.engine == "mlx":
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from lightning_whisper_mlx import LightningWhisperMLX # type: ignore
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log.info("Loading lightning-whisper-mlx model=%s batch=%d", settings.whisper_model, settings.whisper_batch_size)
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self._impl = LightningWhisperMLX(
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model=settings.whisper_model,
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batch_size=settings.whisper_batch_size,
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quant=None,
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)
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elif self.engine == "faster":
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from faster_whisper import WhisperModel # type: ignore
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log.info("Loading faster-whisper model=%s", settings.whisper_model)
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self._impl = WhisperModel(settings.whisper_model, device="auto", compute_type="auto")
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elif self.engine == "mock":
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self._impl = "mock"
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else:
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raise ValueError(f"Unknown WHISPER_ENGINE: {self.engine}")
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def transcribe(self, audio_path: Path, language: str | None = None) -> dict:
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self._ensure_loaded()
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lang = language or settings.whisper_language
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if self.engine == "mock":
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return {
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"text": "[MOCK] Dies ist eine Beispiel-Transkription für Tests ohne Whisper-Modell.",
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"language": lang,
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"segments": [
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{"start": 0.0, "end": 3.0, "text": "[MOCK] Erste Aussage."},
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{"start": 3.0, "end": 6.0, "text": "[MOCK] Zweite Aussage."},
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],
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}
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if self.engine == "mlx":
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out = self._impl.transcribe(audio_path=str(audio_path), language=lang)
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# MLX-Output: {"text": "...", "segments": [...]} oder String. Robust mappen.
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if isinstance(out, str):
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return {"text": out, "language": lang, "segments": []}
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segments = out.get("segments") or []
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normalized = [
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{
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"start": float(s.get("start", 0.0)),
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"end": float(s.get("end", 0.0)),
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"text": (s.get("text") or "").strip(),
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}
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for s in segments
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]
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return {"text": out.get("text", "").strip(), "language": lang, "segments": normalized}
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if self.engine == "faster":
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segments_iter, info = self._impl.transcribe(str(audio_path), language=lang, vad_filter=True)
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segments = []
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full = []
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for s in segments_iter:
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t = s.text.strip()
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full.append(t)
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segments.append({"start": float(s.start), "end": float(s.end), "text": t})
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return {"text": " ".join(full), "language": info.language or lang, "segments": segments}
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raise RuntimeError(f"Engine {self.engine} not implemented")
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engine = WhisperEngine()
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