rorappandClaude Opus 5.5
f90b7df31c
ci / test (push) Successful in 24s
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
39 lines
1.6 KiB
Python
39 lines
1.6 KiB
Python
"""Benchmarks, deren Ergebnisse in Katalog/Registry zurückfließen (Kalibrierung am Deployment-Tag)."""
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from __future__ import annotations
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import time
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from statistics import median
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from typing import Any
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from .audio import rtf, wav_duration
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from .llm import OpenAIChatClient
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DEFAULT_TEXT = (
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"Guten Tag. Dies ist ein Test der Sprachausgabe im Cluster. Wir messen, wie lange die Berechnung "
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"im Verhältnis zur Länge des erzeugten Audios dauert. Zahlen wie 1.250,75 Euro und Daten wie der "
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"3. Oktober 2026 sollen korrekt gesprochen werden."
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)
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def bench_tts(url: str, model: str, *, api_key: str = "", runs: int = 3, text: str | None = None,
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client: OpenAIChatClient | None = None) -> dict[str, Any]:
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client = client or OpenAIChatClient(url, api_key, timeout=600)
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text = text or DEFAULT_TEXT
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results = []
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errors = []
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for _ in range(runs):
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start = time.monotonic()
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try:
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audio = client.speech(model, text) # type: ignore[attr-defined]
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except Exception as exc: # noqa: BLE001
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errors.append(str(exc))
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continue
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elapsed = time.monotonic() - start
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duration = wav_duration(audio)
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results.append({"seconds": round(elapsed, 2), "audio_seconds": round(duration, 2),
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"rtf": round(rtf(elapsed, duration), 3) if duration else None})
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rtfs = [r["rtf"] for r in results if r["rtf"] is not None]
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return {"model": model, "chars": len(text), "runs": results, "errors": errors,
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"rtf_median": round(median(rtfs), 3) if rtfs else None}
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