diff --git a/inference/transnetv2.py b/inference/transnetv2.py
index 92be82c..ee541fa 100644
--- a/inference/transnetv2.py
+++ b/inference/transnetv2.py
@@ -32,7 +32,7 @@ def predict_raw(self, frames: np.ndarray):
 
         return single_frame_pred, all_frames_pred
 
-    def predict_frames(self, frames: np.ndarray):
+    def predict_frames(self, frames: np.ndarray, silent : bool = False):
         assert len(frames.shape) == 4 and frames.shape[1:] == self._input_size, \
             "[TransNetV2] Input shape must be [frames, height, width, 3]."
 
@@ -61,17 +61,22 @@ def input_iterator():
             predictions.append((single_frame_pred.numpy()[0, 25:75, 0],
                                 all_frames_pred.numpy()[0, 25:75, 0]))
 
-            print("\r[TransNetV2] Processing video frames {}/{}".format(
-                min(len(predictions) * 50, len(frames)), len(frames)
-            ), end="")
-        print("")
+            if (not silent):
+
+                print("\r[TransNetV2] Processing video frames {}/{}".format(
+                    min(len(predictions) * 50, len(frames)), len(frames)
+                ), end="")
+
+        if (not silent):
+
+            print("")
 
         single_frame_pred = np.concatenate([single_ for single_, all_ in predictions])
         all_frames_pred = np.concatenate([all_ for single_, all_ in predictions])
 
         return single_frame_pred[:len(frames)], all_frames_pred[:len(frames)]  # remove extra padded frames
 
-    def predict_video(self, video_fn: str):
+    def predict_video(self, video_fn: str, silent : bool = False):
         try:
             import ffmpeg
         except ModuleNotFoundError:
@@ -79,13 +84,16 @@ def predict_video(self, video_fn: str):
                                       "individual frames from video file. Install `ffmpeg` command line tool and then "
                                       "install python wrapper by `pip install ffmpeg-python`.")
 
-        print("[TransNetV2] Extracting frames from {}".format(video_fn))
+        if (not silent):
+
+            print("[TransNetV2] Extracting frames from {}".format(video_fn))
+
         video_stream, err = ffmpeg.input(video_fn).output(
             "pipe:", format="rawvideo", pix_fmt="rgb24", s="48x27"
         ).run(capture_stdout=True, capture_stderr=True)
 
         video = np.frombuffer(video_stream, np.uint8).reshape([-1, 27, 48, 3])
-        return (video, *self.predict_frames(video))
+        return (video, *self.predict_frames(video, silent = silent))
 
     @staticmethod
     def predictions_to_scenes(predictions: np.ndarray, threshold: float = 0.5):