diff --git a/examples/avsr/data_prep/data/data_module.py b/examples/avsr/data_prep/data/data_module.py
index 72bc2e69e7..542e26147a 100644
--- a/examples/avsr/data_prep/data/data_module.py
+++ b/examples/avsr/data_prep/data/data_module.py
@@ -32,8 +32,8 @@ def load_data(self, data_filename, transform=True):
             audio = self.audio_process(audio, sample_rate)
             return audio
         if self.modality == "video":
-            landmarks = self.landmarks_detector(data_filename)
             video = self.load_video(data_filename)
+            landmarks = self.landmarks_detector(video)
             video = self.video_process(video, landmarks)
             video = torch.tensor(video)
             return video
diff --git a/examples/avsr/data_prep/detectors/mediapipe/detector.py b/examples/avsr/data_prep/detectors/mediapipe/detector.py
index 9971dde2b5..e3875c2f2f 100644
--- a/examples/avsr/data_prep/detectors/mediapipe/detector.py
+++ b/examples/avsr/data_prep/detectors/mediapipe/detector.py
@@ -9,7 +9,6 @@
 import mediapipe as mp
 
 import numpy as np
-import torchvision
 
 warnings.filterwarnings("ignore")
 
@@ -29,8 +28,7 @@ def __call__(self, video_frames):
             assert any(l is not None for l in landmarks), "Cannot detect any frames in the video"
         return landmarks
 
-    def detect(self, filename, detector):
-        video_frames = torchvision.io.read_video(filename, pts_unit="sec")[0].numpy()
+    def detect(self, video_frames, detector):
         landmarks = []
         for frame in video_frames:
             results = detector.process(frame)
diff --git a/examples/avsr/data_prep/detectors/retinaface/detector.py b/examples/avsr/data_prep/detectors/retinaface/detector.py
index 2044627045..f35fdf97d2 100644
--- a/examples/avsr/data_prep/detectors/retinaface/detector.py
+++ b/examples/avsr/data_prep/detectors/retinaface/detector.py
@@ -7,7 +7,6 @@
 import warnings
 
 import numpy as np
-import torchvision
 from ibug.face_detection import RetinaFacePredictor
 
 warnings.filterwarnings("ignore")
@@ -19,8 +18,7 @@ def __init__(self, device="cuda:0", model_name="resnet50"):
             device=device, threshold=0.8, model=RetinaFacePredictor.get_model(model_name)
         )
 
-    def __call__(self, filename):
-        video_frames = torchvision.io.read_video(filename, pts_unit="sec")[0].numpy()
+    def __call__(self, video_frames):
         landmarks = []
         for frame in video_frames:
             detected_faces = self.face_detector(frame, rgb=False)