Update app.py

app.py

@@ -114,17 +114,14 @@ class App:
                 with gr.Row():
                     cb_translate_output = gr.Checkbox(value=translation_params["translate_output"], label="Translate output to selected language", info="Translate using Facebook's NLLB",interactive=True)
                 
-#        with gr.Accordion("Speaker diarization", open=False, visible=True):
-#            cb_diarize = gr.Checkbox(value=diarization_params["is_diarize"], label="Use diarization",interactive=True)
-#            tb_hf_token = gr.Text(label="Token", value=diarization_params["hf_token"],info="Required to use diarization")
-#            gr.Markdown("""
-#                        An access token can be created [here](https://hf.co/settings/tokens). If not done yet for your account, you need to accept the terms & conditions of [diarization](https://huggingface.co/pyannote/speaker-diarization-3.1) & [segmentation](https://huggingface.co/pyannote/segmentation-3.0).
-#                        """)
-
         with gr.Accordion("Speaker diarization", open=False, visible=True):
             cb_diarize = gr.Checkbox(value=diarization_params["is_diarize"],label="Use diarization",interactive=True)
             tb_hf_token = gr.Text(label="Token", value=diarization_params["hf_token"],info="An access token is required to use diarization & can be created [here](https://hf.co/settings/tokens). If not done yet for your account, you need to accept the terms & conditions of [diarization](https://huggingface.co/pyannote/speaker-diarization-3.1) & [segmentation](https://huggingface.co/pyannote/segmentation-3.0)")
-
+            dd_diarization_device = gr.Dropdown(label="Device",
+                                                choices=self.whisper_inf.diarizer.get_available_device(),
+                                                value=self.whisper_inf.diarizer.get_device(),
+                                                interactive=True, visible=False)
+            
         with gr.Accordion("Voice Detection Filter", open=False, visible=True):
             cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=vad_params["vad_filter"],
                                         interactive=True,
@@ -147,9 +144,9 @@ class App:
         
         with gr.Accordion("Advanced options", open=False, visible=True):
             with gr.Accordion("Advanced diarization options", open=False, visible=True):    
-                dd_diarization_device = gr.Dropdown(label="Device",
-                                                    choices=self.whisper_inf.diarizer.get_available_device(),
-                                                    value=self.whisper_inf.diarizer.get_device())
+       #         dd_diarization_device = gr.Dropdown(label="Device",
+       #                                             choices=self.whisper_inf.diarizer.get_available_device(),
+       #                                             value=self.whisper_inf.diarizer.get_device())
                                                     
             with gr.Accordion("Advanced processing options", open=False):
                 nb_beam_size = gr.Number(label="Beam Size", value=whisper_params["beam_size"], precision=0, interactive=True,
@@ -235,25 +232,6 @@ class App:
                 cb_uvr_enable_offload = gr.Checkbox(label="Offload sub model after removing background music",
                                                     value=uvr_params["enable_offload"])
     
-#            with gr.Accordion("Voice Detection Filter", open=False):
-#                cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=vad_params["vad_filter"],
-#                                            interactive=True,
-#                                            info="Enable this to transcribe only detected voice parts by submodel.")
-#                sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold",
-#                                         value=vad_params["threshold"],
-#                                         info="Lower it to be more sensitive to small sounds.")
-#                nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0,
-#                                                      value=vad_params["min_speech_duration_ms"],
-#                                                      info="Final speech chunks shorter than this time are thrown out")
-#                nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)",
-#                                                     value=vad_params["max_speech_duration_s"],
-#                                                     info="Maximum duration of speech chunks in \"seconds\".")
-#                nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0,
-#                                                       value=vad_params["min_silence_duration_ms"],
-#                                                       info="In the end of each speech chunk wait for this time"
-#                                                            " before separating it")
-#                nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=vad_params["speech_pad_ms"],
-#                                             info="Final speech chunks are padded by this time each side")
 
         #dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])