Update app.py

app.py

@@ -9,18 +9,14 @@ import subprocess
 import gradio as gr
 from pipelines.pipeline import InferencePipeline
 
-FFMPEG_COMMAND = "-loglevel error -y -r 25 -pix_fmt yuv420p -f mp4"
 pipelines = {
-    "VSR(fast)": InferencePipeline("./configs/LRS3_V_WER19.1.ini", device="cuda:0", face_track=True, detector="mediapipe"),
+    "VSR(mediapipe)": 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(fast)": InferencePipeline("./configs/LRS3_AV_WER0.9.ini", device="cuda:0", face_track=True, detector="mediapipe")
+    "AVSR(mediapipe)": InferencePipeline("./configs/LRS3_AV_WER0.9.ini", device="cuda:0", face_track=True, detector="mediapipe")
 }
 print("Step 0. Model has been loaded.")
 
 def fn(pipeline_type, filename):
-    directory = "./tmp_video"
-    if not os.path.exists(directory):
-        os.makedirs(directory)
     print("Step 0. Video has been uploaded.")
     os.system(command_string)
     selected_pipeline_instance = pipelines[pipeline_type]
@@ -57,7 +53,7 @@ with demo:
             </div>
         """
     )
-    dropdown_list = gr.inputs.Dropdown(["VSR", "ASR", "AVSR", "VSR(fast)", "AVSR(fast)"], label="model")
+    dropdown_list = gr.inputs.Dropdown(["ASR", "VSR(mediapipe)", "AVSR(mediapipe)"], label="model")
     video_file = gr.Video(label="INPUT VIDEO", include_audio=True)
     text = gr.Textbox(label="PREDICTION")
     btn = gr.Button("Submit").style(full_width=True)