add debug function

app.py

@@ -158,10 +158,9 @@ class App:
                                                        hf_token=tb_hf_token,
                                                        diarization_device=dd_diarization_device)
 
-                    # btn_run.click(fn=self.whisper_inf.transcribe_file,
-                    #               inputs=params + whisper_params.as_list(),
-                    #               outputs=[tb_indicator, files_subtitles])
-                    btn_run.click(fn=self.whisper_inf.test, inputs=None, outputs=None)
+                    btn_run.click(fn=self.whisper_inf.transcribe_file,
+                                  inputs=params + whisper_params.as_list(),
+                                  outputs=[tb_indicator, files_subtitles])
                     dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])
 
                 with gr.TabItem("Youtube"):  # tab2

modules/whisper/faster_whisper_inference.py

@@ -33,7 +33,7 @@ class FasterWhisperInference(WhisperBase):
         self.available_compute_types = self.get_available_compute_type()
         self.download_model(model_size="large-v2", model_dir=self.model_dir)
 
-    #@spaces.GPU(duration=120)
+    @spaces.GPU(duration=120)
     def transcribe(self,
                    audio: Union[str, BinaryIO, np.ndarray],
                    progress: gr.Progress,
@@ -65,6 +65,7 @@ class FasterWhisperInference(WhisperBase):
         if params.model_size != self.current_model_size or self.model is None or self.current_compute_type != params.compute_type:
             self.update_model(params.model_size, params.compute_type, progress)
 
+        print("transcribe:")
         segments, info = self.model.transcribe(
             audio=audio,
             language=params.lang,
@@ -89,9 +90,10 @@ class FasterWhisperInference(WhisperBase):
             })
 
         elapsed_time = time.time() - start_time
+        print("transcribe: finished")
         return segments_result, elapsed_time
 
-    #@spaces.GPU(duration=120)
+    @spaces.GPU(duration=120)
     def update_model(self,
                      model_size: str,
                      compute_type: str,
@@ -111,6 +113,7 @@ class FasterWhisperInference(WhisperBase):
             Indicator to show progress directly in gradio.
         """
         progress(0, desc="Initializing Model..")
+        print("update_model:")
         self.current_model_size = self.model_paths[model_size]
         self.current_compute_type = compute_type
         self.model = faster_whisper.WhisperModel(
@@ -119,6 +122,7 @@ class FasterWhisperInference(WhisperBase):
             download_root=self.model_dir,
             compute_type=self.current_compute_type
         )
+        print("update_model: finished")
 
     def get_model_paths(self):
         """
@@ -150,21 +154,18 @@ class FasterWhisperInference(WhisperBase):
             return ['float32', 'int8_float16', 'float16', 'int8', 'int8_float32']
         return ['int16', 'float32', 'int8', 'int8_float32']
 
-    #@spaces.GPU(duration=120)
+    @spaces.GPU(duration=120)
     @staticmethod
     def get_device():
+        print("GET DEVICE:")
         if torch.cuda.is_available():
+            print("GET DEVICE: device is cuda")
             return "cuda"
         elif torch.backends.mps.is_available():
             return "auto"
         else:
             return "cpu"
 
-    @staticmethod
-    @spaces.GPU(duration=120)
-    def test():
-        print("\nWHAT Happend?\n")
-
     @staticmethod
     def download_model(model_size: str, model_dir: str):
         print(f"\nDownloading \"{model_size}\" to \"{model_dir}\"..\n")