diff --git a/whisper_live/server.py b/whisper_live/server.py
index aa4439a..a81f4f3 100644
--- a/whisper_live/server.py
+++ b/whisper_live/server.py
@@ -17,6 +17,7 @@ import torch
 import numpy as np
 import time
 from whisper_live.transcriber import WhisperModel
+from whisper_live.vad import VoiceActivityDetection
 
 
 class TranscriptionServer:
@@ -29,11 +30,7 @@ class TranscriptionServer:
     RATE = 16000
     def __init__(self):
         # voice activity detection model
-        self.vad_model, _ = torch.hub.load(repo_or_dir='snakers4/silero-vad',
-                                           model='silero_vad',
-                                           force_reload=True,
-                                           onnx=True
-                                           )
+        self.vad_model = VoiceActivityDetection()
         self.vad_threshold = 0.4
         self.clients = {}
         self.websockets = {}
diff --git a/whisper_live/vad.py b/whisper_live/vad.py
new file mode 100644
index 0000000..e3a6c88
--- /dev/null
+++ b/whisper_live/vad.py
@@ -0,0 +1,112 @@
+import os
+import subprocess
+import torch
+import numpy as np
+import onnxruntime
+
+
+class VoiceActivityDetection():
+
+    def __init__(self, force_onnx_cpu=True):
+        path = self.download()
+        opts = onnxruntime.SessionOptions()
+        opts.log_severity_level = 3
+
+        opts.inter_op_num_threads = 1
+        opts.intra_op_num_threads = 1
+
+        if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers():
+            self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'], sess_options=opts)
+        else:
+            self.session = onnxruntime.InferenceSession(path, providers=['CUDAExecutionProvider'], sess_options=opts)
+
+        self.reset_states()
+        self.sample_rates = [8000, 16000]
+
+    def _validate_input(self, x, sr: int):
+        if x.dim() == 1:
+            x = x.unsqueeze(0)
+        if x.dim() > 2:
+            raise ValueError(f"Too many dimensions for input audio chunk {x.dim()}")
+
+        if sr != 16000 and (sr % 16000 == 0):
+            step = sr // 16000
+            x = x[:,::step]
+            sr = 16000
+
+        if sr not in self.sample_rates:
+            raise ValueError(f"Supported sampling rates: {self.sample_rates} (or multiply of 16000)")
+
+        if sr / x.shape[1] > 31.25:
+            raise ValueError("Input audio chunk is too short")
+
+        return x, sr
+
+    def reset_states(self, batch_size=1):
+        self._h = np.zeros((2, batch_size, 64)).astype('float32')
+        self._c = np.zeros((2, batch_size, 64)).astype('float32')
+        self._last_sr = 0
+        self._last_batch_size = 0
+
+    def __call__(self, x, sr: int):
+
+        x, sr = self._validate_input(x, sr)
+        batch_size = x.shape[0]
+
+        if not self._last_batch_size:
+            self.reset_states(batch_size)
+        if (self._last_sr) and (self._last_sr != sr):
+            self.reset_states(batch_size)
+        if (self._last_batch_size) and (self._last_batch_size != batch_size):
+            self.reset_states(batch_size)
+
+        if sr in [8000, 16000]:
+            ort_inputs = {'input': x.numpy(), 'h': self._h, 'c': self._c, 'sr': np.array(sr, dtype='int64')}
+            ort_outs = self.session.run(None, ort_inputs)
+            out, self._h, self._c = ort_outs
+        else:
+            raise ValueError()
+
+        self._last_sr = sr
+        self._last_batch_size = batch_size
+
+        out = torch.tensor(out)
+        return out
+
+    def audio_forward(self, x, sr: int, num_samples: int = 512):
+        outs = []
+        x, sr = self._validate_input(x, sr)
+
+        if x.shape[1] % num_samples:
+            pad_num = num_samples - (x.shape[1] % num_samples)
+            x = torch.nn.functional.pad(x, (0, pad_num), 'constant', value=0.0)
+
+        self.reset_states(x.shape[0])
+        for i in range(0, x.shape[1], num_samples):
+            wavs_batch = x[:, i:i+num_samples]
+            out_chunk = self.__call__(wavs_batch, sr)
+            outs.append(out_chunk)
+
+        stacked = torch.cat(outs, dim=1)
+        return stacked.cpu()
+
+    @staticmethod
+    def download(model_url="https://github.com/snakers4/silero-vad/raw/master/files/silero_vad.onnx"):
+        target_dir = os.path.expanduser("~/.cache/whisper-live/")
+
+        # Ensure the target directory exists
+        os.makedirs(target_dir, exist_ok=True)
+
+        # Define the target file path
+        model_filename = os.path.join(target_dir, "silero_vad.onnx")
+
+        # Check if the model file already exists
+        if not os.path.exists(model_filename):
+            # If it doesn't exist, download the model using wget
+            print("Downloading VAD ONNX model...")
+            try:
+                subprocess.run(["wget", "-O", model_filename, model_url], check=True)
+            except subprocess.CalledProcessError:
+                print("Failed to download the model using wget.")
+        return model_filename
+