feat: Transcriber akzeptiert optionales vorgeladenes WhisperModel

tests/test_transcriber.py

@@ -10,7 +10,7 @@ class TestTranscriber:
     @patch("whisper_local.transcriber.WhisperModel")
     def test_init_loads_model(self, mock_model_class):
         t = Transcriber(model_name="small", compute_type="int8", language="de")
-        mock_model_class.assert_called_once_with("small", compute_type="int8")
+        mock_model_class.assert_called_once_with("small", compute_type="int8", download_root=None)
         assert t.language == "de"
 
     @patch("whisper_local.transcriber.WhisperModel")
@@ -55,3 +55,9 @@ class TestTranscriber:
         result = t.transcribe(audio)
 
         assert result == "Hallo Welt"
+
+    def test_init_with_preloaded_model(self):
+        mock_model = MagicMock()
+        t = Transcriber(language="de", model=mock_model)
+        assert t.model is mock_model
+        assert t.language == "de"

whisper_local/transcriber.py

@@ -24,10 +24,21 @@ def _model_cache_dir() -> str | None:
 
 
 class Transcriber:
-    def __init__(self, model_name: str = "small", compute_type: str = "int8", language: str = "de"):
+    def __init__(
+        self,
+        model_name: str = "small",
+        compute_type: str = "int8",
+        language: str = "de",
+        model: WhisperModel | None = None,
+    ):
         self.language = language
+        if model is not None:
+            self.model = model
+        else:
             logger.info("Lade Whisper-Modell '%s' (compute_type=%s)...", model_name, compute_type)
-        self.model = WhisperModel(model_name, compute_type=compute_type, download_root=_model_cache_dir())
+            self.model = WhisperModel(
+                model_name, compute_type=compute_type, download_root=_model_cache_dir()
+            )
             logger.info("Modell geladen")
 
     def transcribe(self, audio: np.ndarray) -> str: