STT: Parakeet-TDT 0.6B v3 als Default-Engine (Review P1-6) — 2,0s -> 0,45s

- onnx-asr (int8, CPU) als neue STT-Engine im Voice-Sidecar; whisper bleibt lazy Fallback
- Engine per VOICE_STT_ENGINE + pro Request (Form-Feld engine) waehlbar
- A/B auf der Box (10,8s DE-Audio): parakeet 0,45s vs whisper-medium 2,44s, Transkript identisch gut
- Live deployt + verifiziert (auch ueber :9001-Proxy: 0,46s)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

voice_service/app.py

@@ -39,10 +39,16 @@ log = logging.getLogger("voice_service")
 # --- Konfiguration (alles über Env überschreibbar; Defaults = Box-Stand) ----------
 PORT = int(os.environ.get("VOICE_PORT", "8650"))
 # STT
-STT_MODEL = os.environ.get("VOICE_STT_MODEL", "medium")       # base|small|medium|large-v3
+# Engine: parakeet (Default, ~10× schneller + bessere Deutsch-WER als whisper-medium,
+# Review 2026-07-02) | whisper (Fallback). Pro Request via Form-Feld `engine` überschreibbar.
+STT_ENGINE = os.environ.get("VOICE_STT_ENGINE", "parakeet")
+STT_MODEL = os.environ.get("VOICE_STT_MODEL", "medium")       # whisper: base|small|medium|large-v3
 STT_DEVICE = os.environ.get("VOICE_STT_DEVICE", "cpu")
 STT_COMPUTE = os.environ.get("VOICE_STT_COMPUTE", "int8")     # int8=CPU-schonend
 STT_LANG = os.environ.get("VOICE_STT_LANG", "de")
+# Parakeet-TDT 0.6B v3 (25 EU-Sprachen inkl. DE, Auto-Language) via onnx-asr.
+PARAKEET_MODEL = os.environ.get("VOICE_PARAKEET_MODEL", "nemo-parakeet-tdt-0.6b-v3")
+PARAKEET_QUANT = os.environ.get("VOICE_PARAKEET_QUANT", "int8")  # ""=fp32
 # Piper (Default-TTS): Verzeichnis mit *.onnx (+ *.onnx.json) Stimmen + das Piper-Binary.
 # Wir nutzen das offizielle Binary (statt des piper-tts-PyPI-Pakets), weil dessen Abhängigkeit
 # piper-phonemize auf neueren Linux-Systemen keine Wheels hat. Das Binary bringt espeak-ng mit.
@@ -113,7 +119,7 @@ def stt_model():
     return _stt
 
 
-def transcribe(audio_bytes: bytes, suffix: str, language: str) -> str:
+def transcribe_whisper(audio_bytes: bytes, suffix: str, language: str) -> str:
     # PyAV (in faster-whisper) dekodiert webm/opus/ogg/wav am robustesten aus einer Datei.
     with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tf:
         tf.write(audio_bytes)
@@ -130,6 +136,65 @@ def transcribe(audio_bytes: bytes, suffix: str, language: str) -> str:
             pass
 
 
+# --- Parakeet-TDT (onnx-asr, lazy Singleton) --------------------------------------
+_parakeet = None
+_parakeet_failed = False
+
+
+def parakeet_model():
+    global _parakeet, _parakeet_failed
+    if _parakeet is None and not _parakeet_failed:
+        try:
+            import onnx_asr
+            kwargs = {"quantization": PARAKEET_QUANT} if PARAKEET_QUANT else {}
+            log.info("Lade Parakeet '%s' (quant=%s) …", PARAKEET_MODEL, PARAKEET_QUANT or "fp32")
+            _parakeet = onnx_asr.load_model(PARAKEET_MODEL, **kwargs)
+        except Exception:
+            # int8-Variante kann je nach Release fehlen → einmal fp32 probieren, dann aufgeben.
+            try:
+                import onnx_asr
+                log.warning("Parakeet quant=%s nicht ladbar, versuche fp32", PARAKEET_QUANT, exc_info=True)
+                _parakeet = onnx_asr.load_model(PARAKEET_MODEL)
+            except Exception:
+                _parakeet_failed = True
+                log.exception("Parakeet nicht verfügbar — STT fällt auf whisper zurück.")
+    return _parakeet
+
+
+def transcribe_parakeet(audio_bytes: bytes, suffix: str) -> str | None:
+    """None = Engine nicht verfügbar (Aufrufer fällt auf whisper zurück).
+    Parakeet v3 erkennt die Sprache selbst; onnx-asr liest nur WAV → Browser-webm/opus
+    erst via PyAV (steckt in faster-whisper) auf 16 kHz mono dekodieren."""
+    model = parakeet_model()
+    if model is None:
+        return None
+    import soundfile as sf
+    from faster_whisper.audio import decode_audio
+    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tf:
+        tf.write(audio_bytes)
+        src = tf.name
+    wav = src + ".wav"
+    try:
+        arr = decode_audio(src, sampling_rate=16000)
+        sf.write(wav, arr, 16000, subtype="PCM_16")
+        return (model.recognize(wav) or "").strip()
+    finally:
+        for p in (src, wav):
+            try:
+                os.unlink(p)
+            except OSError:
+                pass
+
+
+def transcribe(audio_bytes: bytes, suffix: str, language: str, engine: str = "") -> str:
+    eng = (engine or STT_ENGINE).strip().lower()
+    if eng == "parakeet":
+        text = transcribe_parakeet(audio_bytes, suffix)
+        if text is not None:
+            return text
+    return transcribe_whisper(audio_bytes, suffix, language)
+
+
 # =================================================================================
 # TTS — Piper (offizielles Binary; Stimme = ONNX-Datei + .json daneben)
 # =================================================================================
@@ -352,8 +417,13 @@ def edge_list() -> list[dict]:
 async def lifespan(_app: FastAPI):
     logging.basicConfig(level=logging.INFO)
     try:
-        stt_model()  # STT beim Start vorwärmen (Modell aus HF-Cache laden)
+        # Nur die konfigurierte Engine vorwärmen — whisper bleibt als Fallback lazy
-        log.info("Voice-Sidecar bereit (STT '%s', Piper-Dir %s).", STT_MODEL, VOICES_DIR)
+        # (spart ~1,5 GB RAM, solange Parakeet läuft).
+        if STT_ENGINE == "parakeet" and parakeet_model() is not None:
+            log.info("Voice-Sidecar bereit (STT parakeet '%s', Piper-Dir %s).", PARAKEET_MODEL, VOICES_DIR)
+        else:
+            stt_model()
+            log.info("Voice-Sidecar bereit (STT whisper '%s', Piper-Dir %s).", STT_MODEL, VOICES_DIR)
     except Exception:
         log.exception("STT-Vorwärmen fehlgeschlagen (Dienst läuft, /health meldet Detail).")
     yield
@@ -372,7 +442,9 @@ class TTSIn(BaseModel):
 
 @app.get("/health")
 def health() -> dict:
-    return {"ok": True, "stt_model": STT_MODEL,
+    stt_active = "parakeet" if (STT_ENGINE == "parakeet" and not _parakeet_failed) else "whisper"
+    return {"ok": True, "stt_engine": stt_active,
+            "stt_model": PARAKEET_MODEL if stt_active == "parakeet" else STT_MODEL,
             "engines": ["elevenlabs", "edge"],
             "elevenlabs_key": bool(_eleven_key())}
 
@@ -385,12 +457,13 @@ def voices() -> dict:
 
 
 @app.post("/stt")
-async def stt(audio: UploadFile = File(...), language: str = Form(default="")) -> dict:
+async def stt(audio: UploadFile = File(...), language: str = Form(default=""),
+              engine: str = Form(default="")) -> dict:
     data = await audio.read()
     if not data:
         raise HTTPException(400, "Leeres Audio.")
     suffix = Path(audio.filename or "rec.webm").suffix or ".webm"
-    text = transcribe(data, suffix, language or STT_LANG)
+    text = transcribe(data, suffix, language or STT_LANG, engine)
     return {"text": text}
 
 

voice_service/requirements.txt

@@ -8,3 +8,6 @@ soundfile
 numpy
 faster-whisper
 edge-tts
+# STT-Default seit Review 2026-07-02: Parakeet-TDT 0.6B v3 (DE-WER besser + ~10x schneller
+# als whisper-medium auf CPU, via onnx-asr). whisper bleibt als Fallback installiert.
+onnx-asr[cpu,hub]