TTS-Slots (Piper CPU, Qwen3-TTS auf Ada), M4000 außer Betrieb, Engine-Grenzen im Placement (ADR-0006)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

core/src/knaster/bench.py

@@ -0,0 +1,38 @@
+"""Benchmarks, deren Ergebnisse in Katalog/Registry zurückfließen (Kalibrierung am Deployment-Tag)."""
+
+from __future__ import annotations
+
+import time
+from statistics import median
+from typing import Any
+
+from .audio import rtf, wav_duration
+from .llm import OpenAIChatClient
+
+DEFAULT_TEXT = (
+    "Guten Tag. Dies ist ein Test der Sprachausgabe im Cluster. Wir messen, wie lange die Berechnung "
+    "im Verhältnis zur Länge des erzeugten Audios dauert. Zahlen wie 1.250,75 Euro und Daten wie der "
+    "3. Oktober 2026 sollen korrekt gesprochen werden."
+)
+
+
+def bench_tts(url: str, model: str, *, api_key: str = "", runs: int = 3, text: str | None = None,
+              client: OpenAIChatClient | None = None) -> dict[str, Any]:
+    client = client or OpenAIChatClient(url, api_key, timeout=600)
+    text = text or DEFAULT_TEXT
+    results = []
+    errors = []
+    for _ in range(runs):
+        start = time.monotonic()
+        try:
+            audio = client.speech(model, text)  # type: ignore[attr-defined]
+        except Exception as exc:  # noqa: BLE001
+            errors.append(str(exc))
+            continue
+        elapsed = time.monotonic() - start
+        duration = wav_duration(audio)
+        results.append({"seconds": round(elapsed, 2), "audio_seconds": round(duration, 2),
+                        "rtf": round(rtf(elapsed, duration), 3) if duration else None})
+    rtfs = [r["rtf"] for r in results if r["rtf"] is not None]
+    return {"model": model, "chars": len(text), "runs": results, "errors": errors,
+            "rtf_median": round(median(rtfs), 3) if rtfs else None}