Update engines/orpheus_engine.py

engines/orpheus_engine.py

@@ -29,6 +29,7 @@ import logging
 import struct
 import time
 import os
+import torch
 from queue import Queue
 from typing import Generator, Iterable, List, Optional
 
@@ -97,13 +98,13 @@ def _fade_in_out(audio: np.ndarray, fade_ms: int = 50) -> np.ndarray:
 ###############################################################################
 # SNAC – lightweight wrapper                                                 #
 ###############################################################################
-try:
-    from snac import SNAC
-    _snac_model: Optional[SNAC] = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval()
-    _snac_model = _snac_model.to("cuda" if _snac_model and _snac_model.torch.cuda.is_available() else "cpu")
-except Exception as exc:  # pragma: no cover
-    logging.warning("SNAC model could not be loaded – %s", exc)
-    _snac_model = None
+    try:
+        from snac import SNAC
+        _snac_model: Optional[SNAC] = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval()
+        _snac_model = _snac_model.to("cuda" if _snac_model and _snac_model.torch.cuda.is_available() else "cpu")
+    except Exception as exc:  # pragma: no cover
+        logging.warning("SNAC model could not be loaded – %s", exc)
+        _snac_model = None
 
 
 def _codes_to_audio(codes: List[int]) -> bytes:
@@ -171,21 +172,21 @@ class OrpheusEngine(BaseEngine):
         OrpheusVoice("Lea", "f"),
     ]
     def _load_snac(self, model_name: str = SNAC_MODEL):
-    """
-    Lädt den SNAC-Decoder auf CPU/GPU.
-    Fällt bei jedem Fehler sauber auf CPU zurück.
-    """
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    try:
-        snac = SNAC.from_pretrained(model_name).to(device)
-        if device == "cuda":                       # half() nur auf GPU – ältere SNAC-Versionen haben keine .half()
-            snac = snac.half()
-        snac.eval()
-        logging.info(f"SNAC {snac_version} loaded on {device}")
-        return snac
-    except Exception as e:
-        logging.exception("SNAC load failed – running with silent fallback")
-        return None
+        """
+        Lädt den SNAC-Decoder auf CPU/GPU.
+        Fällt bei jedem Fehler sauber auf CPU zurück.
+        """
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        try:
+            snac = SNAC.from_pretrained(model_name).to(device)
+            if device == "cuda":                       # half() nur auf GPU – ältere SNAC-Versionen haben keine .half()
+                snac = snac.half()
+            snac.eval()
+            logging.info(f"SNAC {snac_version} loaded on {device}")
+            return snac
+        except Exception as e:
+            logging.exception("SNAC load failed – running with silent fallback")
+            return None
     # ---------------------------------------------------------------------
     def __init__(
         self,