diff --git a/xtts/docker-compose.yml b/xtts/docker-compose.yml
index c6bf89d..24f4c12 100644
--- a/xtts/docker-compose.yml
+++ b/xtts/docker-compose.yml
@@ -39,7 +39,11 @@ services:
       - RVS_TLS_FALLBACK=${RVS_TLS_FALLBACK:-true}
       - RVS_TOKEN=${RVS_TOKEN}
       - F5TTS_MODEL=${F5TTS_MODEL:-F5TTS_v1_Base}
+      - F5TTS_CKPT_FILE=${F5TTS_CKPT_FILE:-}
+      - F5TTS_VOCAB_FILE=${F5TTS_VOCAB_FILE:-}
       - F5TTS_DEVICE=${F5TTS_DEVICE:-cuda}
+      - F5TTS_CFG_STRENGTH=${F5TTS_CFG_STRENGTH:-2.5}
+      - F5TTS_NFE_STEP=${F5TTS_NFE_STEP:-32}
       - VOICES_DIR=/voices
     restart: unless-stopped
 
diff --git a/xtts/f5tts/bridge.py b/xtts/f5tts/bridge.py
index 737b4ec..769932d 100644
--- a/xtts/f5tts/bridge.py
+++ b/xtts/f5tts/bridge.py
@@ -53,7 +53,14 @@ RVS_TLS_FALLBACK = os.getenv("RVS_TLS_FALLBACK", "true").lower() == "true"
 RVS_TOKEN = os.getenv("RVS_TOKEN", "").strip()
 
 F5TTS_MODEL = os.getenv("F5TTS_MODEL", "F5TTS_v1_Base")
+F5TTS_CKPT_FILE = os.getenv("F5TTS_CKPT_FILE", "")  # optional: HF-Repo oder lokales .pt
+F5TTS_VOCAB_FILE = os.getenv("F5TTS_VOCAB_FILE", "")  # optional: zugehoerige vocab.txt
 F5TTS_DEVICE = os.getenv("F5TTS_DEVICE", "cuda")
+# cfg_strength: wie stark der Generator am Referenz-Voice klebt.
+# Default F5-TTS = 2.0. Bei nicht-EN/CN Sprachen (Deutsch!) hilft >2.5
+# damit das Modell nicht in eine andere Sprache abrutscht.
+F5TTS_CFG_STRENGTH = float(os.getenv("F5TTS_CFG_STRENGTH", "2.5"))
+F5TTS_NFE_STEP = int(os.getenv("F5TTS_NFE_STEP", "32"))
 VOICES_DIR = Path(os.getenv("VOICES_DIR", "/voices"))
 
 PCM_CHUNK_BYTES = 8192   # ~170ms @ 24kHz mono s16
@@ -82,10 +89,17 @@ class F5Runner:
 
     def _load_blocking(self) -> None:
         cls = _get_f5tts_cls()
-        logger.info("Lade F5-TTS '%s' (device=%s)...", F5TTS_MODEL, F5TTS_DEVICE)
+        logger.info("Lade F5-TTS '%s' (device=%s, ckpt=%s)...",
+                    F5TTS_MODEL, F5TTS_DEVICE, F5TTS_CKPT_FILE or "default")
         t0 = time.time()
-        self.model = cls(model=F5TTS_MODEL, device=F5TTS_DEVICE)
-        logger.info("F5-TTS geladen in %.1fs", time.time() - t0)
+        kwargs = {"model": F5TTS_MODEL, "device": F5TTS_DEVICE}
+        if F5TTS_CKPT_FILE:
+            kwargs["ckpt_file"] = F5TTS_CKPT_FILE
+        if F5TTS_VOCAB_FILE:
+            kwargs["vocab_file"] = F5TTS_VOCAB_FILE
+        self.model = cls(**kwargs)
+        logger.info("F5-TTS geladen in %.1fs (cfg_strength=%.1f, nfe=%d)",
+                    time.time() - t0, F5TTS_CFG_STRENGTH, F5TTS_NFE_STEP)
 
     async def ensure_loaded(self) -> None:
         async with self._lock:
@@ -95,12 +109,18 @@ class F5Runner:
             await loop.run_in_executor(None, self._load_blocking)
 
     def _infer_blocking(self, gen_text: str, ref_wav: str, ref_text: str) -> tuple[np.ndarray, int]:
+        # cfg_strength + nfe_step erhoeht damit das Modell nicht in andere
+        # Sprachen abrutscht (Bug bei Deutsch: rutscht ohne Verstaerkung
+        # gerne ins Spanische ab, weil F5TTS_v1_Base hauptsaechlich auf EN+CN
+        # trainiert ist).
         wav, sr, _ = self.model.infer(
             ref_file=ref_wav,
             ref_text=ref_text,
             gen_text=gen_text,
             remove_silence=True,
             seed=-1,
+            cfg_strength=F5TTS_CFG_STRENGTH,
+            nfe_step=F5TTS_NFE_STEP,
         )
         # F5-TTS gibt float32 1D-Array — auf 24kHz sample-rate standard
         if not isinstance(wav, np.ndarray):