Squash Odysseus development history

services/stt/stt_service.py

@@ -40,6 +40,8 @@ class STTService:
     @property
     def available(self) -> bool:
         settings = self._load_settings()
+        if settings.get("stt_enabled") is False:
+            return False
         provider = settings["stt_provider"]
         if provider == "disabled":
             return False
@@ -57,17 +59,29 @@ class STTService:
         if self._whisper_model is None:
             try:
                 from faster_whisper import WhisperModel
-                settings = self._load_settings()
-                model_size = settings.get("stt_model", "base")
-                # Use CPU by default; will use CUDA if available
-                import torch
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-                compute_type = "float16" if device == "cuda" else "int8"
-                self._whisper_model = WhisperModel(model_size, device=device, compute_type=compute_type)
-                logger.info(f"faster-whisper model '{model_size}' loaded on {device}")
             except ImportError:
                 logger.warning("faster-whisper not installed. Install with: pip install faster-whisper")
                 return None
+            try:
+                settings = self._load_settings()
+                model_size = settings.get("stt_model", "base")
+                # faster-whisper runs on CTranslate2, not torch. torch is only
+                # used (optionally) to detect a CUDA device for acceleration —
+                # if it's missing or unusable we just run on CPU. Keeping this
+                # probe separate (and tolerant of any failure, e.g. a broken
+                # CUDA/torch install that raises OSError on import) means a
+                # torch-less or torch-broken machine still does CPU
+                # transcription instead of failing with a misleading
+                # "faster-whisper not installed" error.
+                try:
+                    import torch
+                    use_cuda = torch.cuda.is_available()
+                except Exception:
+                    use_cuda = False
+                device = "cuda" if use_cuda else "cpu"
+                compute_type = "float16" if device == "cuda" else "int8"
+                self._whisper_model = WhisperModel(model_size, device=device, compute_type=compute_type)
+                logger.info(f"faster-whisper model '{model_size}' loaded on {device}")
             except Exception as e:
                 logger.error(f"Failed to load whisper model: {e}")
                 return None
@@ -77,6 +91,7 @@ class STTService:
         model = self._get_whisper()
         if not model:
             return None
+        tmp_path = None
         try:
             # Write to temp file (faster-whisper needs a file path or file-like)
             with tempfile.NamedTemporaryFile(suffix=".webm", delete=False) as tmp:
@@ -90,14 +105,14 @@ class STTService:
             segments, info = model.transcribe(tmp_path, **kwargs)
             text = " ".join(seg.text.strip() for seg in segments)
 
-            # Cleanup
-            Path(tmp_path).unlink(missing_ok=True)
-
             logger.info(f"Local STT: {len(text)} chars, lang={info.language}, prob={info.language_probability:.2f}")
             return text
         except Exception as e:
             logger.error(f"Local STT transcription failed: {e}", exc_info=True)
             return None
+        finally:
+            if tmp_path:
+                Path(tmp_path).unlink(missing_ok=True)
 
     # ── API endpoint ──
 
@@ -140,6 +155,8 @@ class STTService:
 
     def transcribe(self, audio_bytes: bytes) -> Optional[str]:
         settings = self._load_settings()
+        if settings.get("stt_enabled") is False:
+            return None
         provider = settings["stt_provider"]
         model = settings["stt_model"]
         language = settings.get("stt_language", "")