local vad model

whisperx/vad.py

@@ -15,33 +15,29 @@ from tqdm import tqdm
 
 from .diarize import Segment as SegmentX
 
+# deprecated
 VAD_SEGMENTATION_URL = "https://whisperx.s3.eu-west-2.amazonaws.com/model_weights/segmentation/0b5b3216d60a2d32fc086b47ea8c67589aaeb26b7e07fcbe620d6d0b83e209ea/pytorch_model.bin"
 
 def load_vad_model(device, vad_onset=0.500, vad_offset=0.363, use_auth_token=None, model_fp=None):
     model_dir = torch.hub._get_torch_home()
+
+    vad_dir = os.path.dirname(os.path.abspath(__file__))
+
     os.makedirs(model_dir, exist_ok = True)
     if model_fp is None:
-        model_fp = os.path.join(model_dir, "whisperx-vad-segmentation.bin")
+        # Dynamically resolve the path to the model file
+        model_fp = os.path.join(vad_dir, "..", "models", "pytorch_model.bin")
+        model_fp = os.path.abspath(model_fp)  # Ensure the path is absolute
+    else:
+        model_fp = os.path.abspath(model_fp)  # Ensure any provided path is absolute
+    
+    # Check if the resolved model file exists
+    if not os.path.exists(model_fp):
+        raise FileNotFoundError(f"Model file not found at {model_fp}")
+    
     if os.path.exists(model_fp) and not os.path.isfile(model_fp):
         raise RuntimeError(f"{model_fp} exists and is not a regular file")
 
-    if not os.path.isfile(model_fp):
-        with urllib.request.urlopen(VAD_SEGMENTATION_URL) as source, open(model_fp, "wb") as output:
-            with tqdm(
-                total=int(source.info().get("Content-Length")),
-                ncols=80,
-                unit="iB",
-                unit_scale=True,
-                unit_divisor=1024,
-            ) as loop:
-                while True:
-                    buffer = source.read(8192)
-                    if not buffer:
-                        break
-
-                    output.write(buffer)
-                    loop.update(len(buffer))
-
     model_bytes = open(model_fp, "rb").read()
     if hashlib.sha256(model_bytes).hexdigest() != VAD_SEGMENTATION_URL.split('/')[-2]:
         raise RuntimeError(