feat: Abschreiben im Cluster mit Whisper-Image und Voice-CronJob (T7 L3 Task 13)

ApiCore-Whisper: transcribe.py liest das Manifest im RAM-Arbeitsordner,
dekodiert mit festem Demuxer je Endung, misst die Länge vor dem
Abschreiben (TOO_LONG > 240 s, DECODE_FAILED bei Länge 0), sperrt das
Netz vor dem Modell-Laden und schreibt nur Zähler auf stdout. Image:
python:3.12-slim per Digest, Nutzer 1000, alle Pakete mit Hash
(faster-whisper 1.2.1, av 18.1.0 wegen Inkompatibilität mit av 19),
Modell faster-whisper-small auf fester Revision mit SHA-256 je Datei.
CI-Workflow nur bei Änderungen unter ApiCore-Whisper/, Actions per SHA.

deploy/k8s/components/voice: CronJob alle 2 Minuten mit fetch, whisper,
ingest (gleiche UID, getrennte /tmp-Volumes, damit der Decoder dem
Container mit den Secrets nichts unterschieben kann; whisper ohne
Secrets und Umgebungsvariablen außer WHISPER_CPU_THREADS), Overlay mit
Platzhalter-Digest, ConfigMap-Werte für Sprache, Policy-Tests (55).
Die Anbindung des Anthropic-Schlüssels bleibt offen (Ruling 9).

Refs #27

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>

ApiCore-Whisper/fetch_model.py

@@ -0,0 +1,69 @@
+"""Downloads the Whisper model files at build time and verifies each one.
+
+Used only in the Dockerfile (``model`` stage). The revision is a fixed commit
+of the Hugging Face repository and every file has a pinned SHA-256: a changed
+or tampered file stops the build. At runtime the container has no network and
+``HF_HUB_OFFLINE=1``.
+
+Model: Systran/faster-whisper-small (CTranslate2 conversion of OpenAI Whisper
+``small``, MIT licence). The weights are stored as float16; faster-whisper
+quantises them to int8 on load (``compute_type='int8'``).
+"""
+
+from __future__ import annotations
+
+import hashlib
+import sys
+import urllib.request
+from pathlib import Path
+
+REPOSITORY = "Systran/faster-whisper-small"
+REVISION = "536b0662742c02347bc0e980a01041f333bce120"
+BASE_URL = f"https://huggingface.co/{REPOSITORY}/resolve/{REVISION}"
+FILES: dict[str, str] = {
+    "config.json": "b55496ac7940a7ae47d2c01eab40edfd8701feec1229d9cce3b40014383fb828",
+    "model.bin": "3e305921506d8872816023e4c273e75d2419fb89b24da97b4fe7bce14170d671",
+    "tokenizer.json": "fb7b63191e9bb045082c79fd742a3106a12c99513ab30df4a0d47fa6cb6fd0ab",
+    "vocabulary.txt": "34ce3fe1c5041027b3f8d42912270993f986dbc4bb34cf27f951e34a1e453913",
+}
+CHUNK_BYTES = 1024 * 1024
+TIMEOUT_SEC = 120
+
+
+class ChecksumMismatch(Exception):
+    """A downloaded file does not have the pinned SHA-256."""
+
+
+def sha256_of(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(CHUNK_BYTES), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def verify(path: Path, expected: str) -> None:
+    actual = sha256_of(path)
+    if actual != expected:
+        raise ChecksumMismatch(f"{path.name}: expected {expected}, got {actual}")
+
+
+def download(url: str, target: Path) -> None:
+    if not url.startswith("https://"):
+        raise ValueError("only https downloads are allowed")
+    with urllib.request.urlopen(url, timeout=TIMEOUT_SEC) as response, target.open("wb") as out:  # noqa: S310
+        for chunk in iter(lambda: response.read(CHUNK_BYTES), b""):
+            out.write(chunk)
+
+
+def fetch_all(target_dir: Path, files: dict[str, str] = FILES, base_url: str = BASE_URL) -> None:
+    target_dir.mkdir(parents=True, exist_ok=True)
+    for name, expected in files.items():
+        target = target_dir / name
+        download(f"{base_url}/{name}", target)
+        verify(target, expected)
+        print(f"ok {name}")
+
+
+if __name__ == "__main__":
+    fetch_all(Path(sys.argv[1] if len(sys.argv) > 1 else "/models/small"))