Update app.py

app.py

@@ -11,10 +11,8 @@ from pipelines.pipeline import InferencePipeline
 
 FFMPEG_COMMAND = "-loglevel error -y -r 25 -pix_fmt yuv420p -f mp4"
 pipelines = {
-    "VSR": InferencePipeline("./configs/LRS3_V_WER19.1.ini", device="cuda:0", face_track=True, detector="retinaface"),
     "VSR(fast)": InferencePipeline("./configs/LRS3_V_WER19.1.ini", device="cuda:0", face_track=True, detector="mediapipe"),
     "ASR": InferencePipeline("./configs/LRS3_A_WER1.0.ini", device="cuda:0", face_track=True, detector="retinaface"),
-    "AVSR": InferencePipeline("./configs/LRS3_AV_WER0.9.ini", device="cuda:0", face_track=True, detector="retinaface"),
     "AVSR(fast)": InferencePipeline("./configs/LRS3_AV_WER0.9.ini", device="cuda:0", face_track=True, detector="mediapipe")
 }
 print("Step 0. Model has been loaded.")
@@ -23,17 +21,13 @@ def fn(pipeline_type, filename):
     directory = "./tmp_video"
     if not os.path.exists(directory):
         os.makedirs(directory)
-    now = datetime.datetime.now()
-    timestamp = now.strftime("%Y-%m-%d_%H-%M-%S")
-    dst_filename = f"{directory}/file_{timestamp}.mp4"
-    command_string = f"ffmpeg -i {filename} {FFMPEG_COMMAND} {dst_filename}"
     print("Step 0. Video has been uploaded.")
     os.system(command_string)
     selected_pipeline_instance = pipelines[pipeline_type]
     print("Step 1. Video has been converted.")
-    landmarks = selected_pipeline_instance.process_landmarks(dst_filename, landmarks_filename=None)
+    landmarks = selected_pipeline_instance.process_landmarks(filename, landmarks_filename=None)
     print("Step 2. Landmarks have been detected.")
-    data = selected_pipeline_instance.dataloader.load_data(dst_filename, landmarks)
+    data = selected_pipeline_instance.dataloader.load_data(filename, landmarks)
     print("Step 3. Data has been preprocessed.")
     transcript = selected_pipeline_instance.model.infer(data)
     print("Step 4. Inference has been done.")
@@ -74,8 +68,9 @@ with demo:
         gr.HTML(
             """
             <div class="acknowledgments">
+                <p> We used retinaface for training, but for the demo we used mediapipe </p>
                 <p> We share this demo only for non-commercial purposes. </p>
-                        </div>
+            </div>
             """
         )