diff --git a/tests/test_vad.py b/tests/test_vad.py
index cb3dc05..eb0e35f 100644
--- a/tests/test_vad.py
+++ b/tests/test_vad.py
@@ -1,13 +1,13 @@
-from modules.utils.paths import *
-from modules.whisper.whisper_factory import WhisperFactory
-from modules.whisper.data_classes import *
-from test_config import *
-from test_transcription import download_file, test_transcribe
-
 import gradio as gr
 import pytest
 import os
 
+from modules.whisper.data_classes import *
+from modules.vad.silero_vad import SileroVAD
+from test_config import *
+from test_transcription import download_file, test_transcribe
+from faster_whisper.vad import VadOptions, get_speech_timestamps
+
 
 @pytest.mark.parametrize(
     "whisper_type,vad_filter,bgm_separation,diarization",
@@ -24,3 +24,35 @@ def test_vad_pipeline(
     diarization: bool,
 ):
     test_transcribe(whisper_type, vad_filter, bgm_separation, diarization)
+
+
+@pytest.mark.parametrize(
+    "threshold,min_speech_duration_ms,min_silence_duration_ms",
+    [
+        (0.5, 250, 2000),
+    ]
+)
+def test_vad(
+    threshold: float,
+    min_speech_duration_ms: int,
+    min_silence_duration_ms: int
+):
+    audio_path_dir = os.path.join(WEBUI_DIR, "tests")
+    audio_path = os.path.join(audio_path_dir, "jfk.wav")
+
+    if not os.path.exists(audio_path):
+        download_file(TEST_FILE_DOWNLOAD_URL, audio_path_dir)
+
+    vad_model = SileroVAD()
+    vad_model.update_model()
+
+    audio, speech_chunks = vad_model.run(
+        audio=audio_path,
+        vad_parameters=VadOptions(
+            threshold=threshold,
+            min_silence_duration_ms=min_silence_duration_ms,
+            min_speech_duration_ms=min_speech_duration_ms
+        )
+    )
+
+    assert speech_chunks