diff --git a/whisperx/asr.py b/whisperx/asr.py
index 43976f2..52e7972 100644
--- a/whisperx/asr.py
+++ b/whisperx/asr.py
@@ -13,8 +13,8 @@ from transformers import Pipeline
 from transformers.pipelines.pt_utils import PipelineIterator
 
 from .audio import N_SAMPLES, SAMPLE_RATE, load_audio, log_mel_spectrogram
-import whisperx.vads
 from .types import SingleSegment, TranscriptionResult
+from .vads import Vad, Silero, Pyannote
 
 def find_numeral_symbol_tokens(tokenizer):
     numeral_symbol_tokens = []
@@ -209,12 +209,12 @@ class FasterWhisperPipeline(Pipeline):
 
         # Pre-process audio and merge chunks as defined by the respective VAD child class 
         # In case vad_model is manually assigned (see 'load_model') follow the functionality of pyannote toolkit
-        if issubclass(type(self.vad_model), whisperx.vads.Vad):
+        if issubclass(type(self.vad_model), Vad):
             waveform = self.vad_model.preprocess_audio(audio)
             merge_chunks =  self.vad_model.merge_chunks
         else:
-            waveform = whisperx.vads.Pyannote.preprocess_audio(audio)
-            merge_chunks = whisperx.vads.Pyannote.merge_chunks
+            waveform = Pyannote.preprocess_audio(audio)
+            merge_chunks = Pyannote.merge_chunks
 
         vad_segments = self.vad_model({"waveform": waveform, "sample_rate": SAMPLE_RATE})
         vad_segments = merge_chunks(
@@ -398,9 +398,9 @@ def load_model(
         vad_model = vad_model
     else:
         if vad_method == "silero":
-            vad_model = whisperx.vads.Silero(**default_vad_options)
+            vad_model = Silero(**default_vad_options)
         elif vad_method == "pyannote":
-            vad_model = whisperx.vads.Pyannote(torch.device(device), use_auth_token=None, **default_vad_options)
+            vad_model = Pyannote(torch.device(device), use_auth_token=None, **default_vad_options)
         else:
             raise ValueError(f"Invalid vad_method: {vad_method}")