Update app.py

app.py

@@ -16,14 +16,12 @@ class App:
         self.args = args
         self.app = gr.Blocks(css=CSS, theme=self.args.theme)
         self.whisper_inf = self.init_whisper()
-        print(f"Use \"{self.args.whisper_type}\" implementation")
-        print(f"Device \"{self.whisper_inf.device}\" is detected")
         self.nllb_inf = NLLBInference()
         self.deepl_api = DeepLAPI()
+        self.log_initialization()
 
     def init_whisper(self):
         whisper_type = self.args.whisper_type.lower().strip()
-
         if whisper_type in ["faster_whisper", "faster-whisper"]:
             whisper_inf = FasterWhisperInference()
             whisper_inf.model_dir = self.args.faster_whisper_model_dir
@@ -35,26 +33,27 @@ class App:
             whisper_inf.model_dir = self.args.faster_whisper_model_dir
         return whisper_inf
 
+    def log_initialization(self):
+        print(f'Use "{self.args.whisper_type}" implementation')
+        print(f'Device "{self.whisper_inf.device}" is detected')
+
     @staticmethod
     def open_folder(folder_path: str):
         if os.path.exists(folder_path):
-            os.system(f"start {folder_path}")
+            os.system(f'start {folder_path}')
         else:
-            print(f"The folder {folder_path} does not exist.")
+            print(f'The folder {folder_path} does not exist.')
 
     @staticmethod
     def on_change_models(model_size: str):
         translatable_model = ["large", "large-v1", "large-v2", "large-v3"]
-        if model_size not in translatable_model:
-            return gr.Checkbox(visible=False, value=False, interactive=False)
-        else:
-            return gr.Checkbox(visible=True, value=False, label="Translate to English?", interactive=True)
+        return gr.Checkbox(visible=model_size in translatable_model, value=False, label="Translate to English?", interactive=True)
 
     def transcribe_file_wrapper(self, file, file_format, timestamp, *whisper_params):
         try:
             result, output_file = self.whisper_inf.transcribe_file(file, file_format, timestamp, *whisper_params)
             if not os.path.exists(output_file):
-                raise FileNotFoundError(f"Output file {output_file} does not exist.")
+                raise FileNotFoundError(f'Output file {output_file} does not exist.')
             return result, output_file
         except Exception as e:
             return str(e), None
@@ -65,285 +64,279 @@ class App:
                 with gr.Column():
                     gr.Markdown(MARKDOWN, elem_id="md_project")
             with gr.Tabs():
-                with gr.TabItem("File"):  # tab1
-                    with gr.Row():
-                        input_file = gr.Files(type="filepath", label="Upload File here")
-                    with gr.Row():
-                        dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-v2",
-                                               label="Model")
-                        dd_lang = gr.Dropdown(choices=["Automatic Detection"] + self.whisper_inf.available_langs,
-                                              value="Automatic Detection", label="Language")
-                        dd_file_format = gr.Dropdown(["SRT", "WebVTT", "txt"], value="SRT", label="File Format")
-                    with gr.Row():
-                        cb_translate = gr.Checkbox(value=False, label="Translate to English?", interactive=True)
-                    with gr.Row():
-                        cb_timestamp = gr.Checkbox(value=True, label="Add a timestamp to the end of the filename", interactive=True)
-                    with gr.Accordion("VAD Options", open=False, visible=isinstance(self.whisper_inf, FasterWhisperInference)):
-                        cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
-                        sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5)
-                        nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250)
-                        nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999)
-                        nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000)
-                        nb_window_size_sample = gr.Number(label="Window Size (samples)", precision=0, value=1024)
-                        nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
-                    with gr.Accordion("Advanced_Parameters", open=False):
-                        nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
-                        nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
-                        nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
-                        dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True)
-                        nb_best_of = gr.Number(label="Best Of", value=5, interactive=True)
-                        nb_patience = gr.Number(label="Patience", value=1, interactive=True)
-                        cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True)
-                        tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True)
-                        sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True)
-                        nb_compression_ratio_threshold = gr.Number(label="Compression Ratio Threshold", value=2.4, interactive=True)
-                    with gr.Row():
-                        btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
-                    with gr.Row():
-                        tb_indicator = gr.Textbox(label="Output", scale=5)
-                        files_subtitles = gr.Files(label="Downloadable output file", scale=3, interactive=False)
-                        btn_openfolder = gr.Button('📂', scale=1)
+                with gr.TabItem("File"):
+                    self.build_file_tab()
+                with gr.TabItem("Youtube"):
+                    self.build_youtube_tab()
+                with gr.TabItem("Mic"):
+                    self.build_mic_tab()
+                with gr.TabItem("T2T Translation"):
+                    self.build_t2t_translation_tab()
 
-                    params = [input_file, dd_file_format, cb_timestamp]
-                    whisper_params = WhisperGradioComponents(model_size=dd_model,
-                                                             lang=dd_lang,
-                                                             is_translate=cb_translate,
-                                                             beam_size=nb_beam_size,
-                                                             log_prob_threshold=nb_log_prob_threshold,
-                                                             no_speech_threshold=nb_no_speech_threshold,
-                                                             compute_type=dd_compute_type,
-                                                             best_of=nb_best_of,
-                                                             patience=nb_patience,
-                                                             condition_on_previous_text=cb_condition_on_previous_text,
-                                                             initial_prompt=tb_initial_prompt,
-                                                             temperature=sd_temperature,
-                                                             compression_ratio_threshold=nb_compression_ratio_threshold,
-                                                             vad_filter=cb_vad_filter,
-                                                             threshold=sd_threshold,
-                                                             min_speech_duration_ms=nb_min_speech_duration_ms,
-                                                             max_speech_duration_s=nb_max_speech_duration_s,
-                                                             min_silence_duration_ms=nb_min_silence_duration_ms,
-                                                             window_size_sample=nb_window_size_sample,
-                                                             speech_pad_ms=nb_speech_pad_ms)
+        # Launch the app with optional Gradio settings
+        launch_args = {}
+        if self.args.share:
+            launch_args['share'] = self.args.share
+        if self.args.server_name:
+            launch_args['server_name'] = self.args.server_name
+        if self.args.server_port:
+            launch_args['server_port'] = self.args.server_port
+        if self.args.username and self.args.password:
+            launch_args['auth'] = (self.args.username, self.args.password)
+        launch_args['inbrowser'] = True
 
-                    btn_run.click(fn=self.transcribe_file_wrapper,
-                                  inputs=params + whisper_params.to_list(),
-                                  outputs=[tb_indicator, files_subtitles])
-                    btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
-                    dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])
+        self.app.queue(api_open=self.args.api_open).launch(**launch_args)
 
-                with gr.TabItem("Youtube"):  # tab2
-                    with gr.Row():
-                        tb_youtubelink = gr.Textbox(label="Youtube Link")
-                    with gr.Row(equal_height=True):
-                        with gr.Column():
-                            img_thumbnail = gr.Image(label="Youtube Thumbnail")
-                        with gr.Column():
-                            tb_title = gr.Label(label="Youtube Title")
-                            tb_description = gr.Textbox(label="Youtube Description", max_lines=15)
-                    with gr.Row():
-                        dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-v2",
-                                               label="Model")
-                        dd_lang = gr.Dropdown(choices=["Automatic Detection"] + self.whisper_inf.available_langs,
-                                              value="Automatic Detection", label="Language")
-                        dd_file_format = gr.Dropdown(choices=["SRT", "WebVTT", "txt"], value="SRT", label="File Format")
-                    with gr.Row():
-                        cb_translate = gr.Checkbox(value=False, label="Translate to English?", interactive=True)
-                    with gr.Row():
-                        cb_timestamp = gr.Checkbox(value=True, label="Add a timestamp to the end of the filename",
-                                                   interactive=True)
-                    with gr.Accordion("VAD Options", open=False, visible=isinstance(self.whisper_inf, FasterWhisperInference)):
-                        cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
-                        sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5)
-                        nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250)
-                        nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999)
-                        nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000)
-                        nb_window_size_sample = gr.Number(label="Window Size (samples)", precision=0, value=1024)
-                        nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
-                    with gr.Accordion("Advanced_Parameters", open=False):
-                        nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
-                        nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
-                        nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
-                        dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True)
-                        nb_best_of = gr.Number(label="Best Of", value=5, interactive=True)
-                        nb_patience = gr.Number(label="Patience", value=1, interactive=True)
-                        cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True)
-                        tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True)
-                        sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True)
-                        nb_compression_ratio_threshold = gr.Number(label="Compression Ratio Threshold", value=2.4, interactive=True)
-                    with gr.Row():
-                        btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
-                    with gr.Row():
-                        tb_indicator = gr.Textbox(label="Output", scale=5)
-                        files_subtitles = gr.Files(label="Downloadable output file", scale=3)
-                        btn_openfolder = gr.Button('📂', scale=1)
+    def build_file_tab(self):
+        with gr.Row():
+            input_file = gr.Files(type="filepath", label="Upload File here")
+        with gr.Row():
+            dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-v2", label="Model")
+            dd_lang = gr.Dropdown(choices=["Automatic Detection"] + self.whisper_inf.available_langs, value="Automatic Detection", label="Language")
+            dd_file_format = gr.Dropdown(choices=["SRT", "WebVTT", "txt"], value="SRT", label="File Format")
+        with gr.Row():
+            cb_translate = gr.Checkbox(value=False, label="Translate to English?", interactive=True)
+        with gr.Row():
+            cb_timestamp = gr.Checkbox(value=True, label="Add a timestamp to the end of the filename", interactive=True)
+        with gr.Accordion("VAD Options", open=False, visible=isinstance(self.whisper_inf, FasterWhisperInference)):
+            cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
+            sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5)
+            nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250)
+            nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999)
+            nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000)
+            nb_window_size_sample = gr.Number(label="Window Size (samples)", precision=0, value=1024)
+            nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
+        with gr.Accordion("Advanced_Parameters", open=False):
+            nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
+            nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
+            nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
+            dd_compute_type = gr.Dropdown(choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, label="Compute Type", interactive=True)
+            nb_best_of = gr.Number(label="Best Of", value=5, interactive=True)
+            nb_patience = gr.Number(label="Patience", value=1, interactive=True)
+            cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True)
+            tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True)
+            sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True)
+            nb_compression_ratio_threshold = gr.Number(label="Compression Ratio Threshold", value=2.4, interactive=True)
+        with gr.Row():
+            btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
+        with gr.Row():
+            tb_indicator = gr.Textbox(label="Output", scale=5)
+            files_subtitles = gr.Files(label="Downloadable output file", scale=3, interactive=False)
+            btn_openfolder = gr.Button('📂', scale=1)
 
-                    params = [tb_youtubelink, dd_file_format, cb_timestamp]
-                    whisper_params = WhisperGradioComponents(model_size=dd_model,
-                                                             lang=dd_lang,
-                                                             is_translate=cb_translate,
-                                                             beam_size=nb_beam_size,
-                                                             log_prob_threshold=nb_log_prob_threshold,
-                                                             no_speech_threshold=nb_no_speech_threshold,
-                                                             compute_type=dd_compute_type,
-                                                             best_of=nb_best_of,
-                                                             patience=nb_patience,
-                                                             condition_on_previous_text=cb_condition_on_previous_text,
-                                                             initial_prompt=tb_initial_prompt,
-                                                             temperature=sd_temperature,
-                                                             compression_ratio_threshold=nb_compression_ratio_threshold,
-                                                             vad_filter=cb_vad_filter,
-                                                             threshold=sd_threshold,
-                                                             min_speech_duration_ms=nb_min_speech_duration_ms,
-                                                             max_speech_duration_s=nb_max_speech_duration_s,
-                                                             min_silence_duration_ms=nb_min_silence_duration_ms,
-                                                             window_size_sample=nb_window_size_sample,
-                                                             speech_pad_ms=nb_speech_pad_ms)
-                    btn_run.click(fn=self.whisper_inf.transcribe_youtube,
-                                  inputs=params + whisper_params.to_list(),
-                                  outputs=[tb_indicator, files_subtitles])
-                    tb_youtubelink.change(get_ytmetas, inputs=[tb_youtubelink],
-                                          outputs=[img_thumbnail, tb_title, tb_description])
-                    btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
-                    dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])
+        params = [input_file, dd_file_format, cb_timestamp]
+        whisper_params = WhisperGradioComponents(model_size=dd_model,
+                                                 lang=dd_lang,
+                                                 is_translate=cb_translate,
+                                                 beam_size=nb_beam_size,
+                                                 log_prob_threshold=nb_log_prob_threshold,
+                                                 no_speech_threshold=nb_no_speech_threshold,
+                                                 compute_type=dd_compute_type,
+                                                 best_of=nb_best_of,
+                                                 patience=nb_patience,
+                                                 condition_on_previous_text=cb_condition_on_previous_text,
+                                                 initial_prompt=tb_initial_prompt,
+                                                 temperature=sd_temperature,
+                                                 compression_ratio_threshold=nb_compression_ratio_threshold,
+                                                 vad_filter=cb_vad_filter,
+                                                 threshold=sd_threshold,
+                                                 min_speech_duration_ms=nb_min_speech_duration_ms,
+                                                 max_speech_duration_s=nb_max_speech_duration_s,
+                                                 min_silence_duration_ms=nb_min_silence_duration_ms,
+                                                 window_size_sample=nb_window_size_sample,
+                                                 speech_pad_ms=nb_speech_pad_ms)
 
-                with gr.TabItem("Mic"):  # tab3
-                    with gr.Row():
-                        mic_input = gr.Microphone(label="Record with Mic", type="filepath", interactive=True)
-                    with gr.Row():
-                        dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-v2",
-                                               label="Model")
-                        dd_lang = gr.Dropdown(choices=["Automatic Detection"] + self.whisper_inf.available_langs,
-                                              value="Automatic Detection", label="Language")
-                        dd_file_format = gr.Dropdown(["SRT", "WebVTT", "txt"], value="SRT", label="File Format")
-                    with gr.Row():
-                        cb_translate = gr.Checkbox(value=False, label="Translate to English?", interactive=True)
-                    with gr.Accordion("VAD Options", open=False, visible=isinstance(self.whisper_inf, FasterWhisperInference)):
-                        cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
-                        sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5)
-                        nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250)
-                        nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999)
-                        nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000)
-                        nb_window_size_sample = gr.Number(label="Window Size (samples)", precision=0, value=1024)
-                        nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
-                    with gr.Accordion("Advanced_Parameters", open=False):
-                        nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
-                        nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
-                        nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
-                        dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True)
-                        nb_best_of = gr.Number(label="Best Of", value=5, interactive=True)
-                        nb_patience = gr.Number(label="Patience", value=1, interactive=True)
-                        cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True)
-                        tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True)
-                        sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True)
-                    with gr.Row():
-                        btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
-                    with gr.Row():
-                        tb_indicator = gr.Textbox(label="Output", scale=5)
-                        files_subtitles = gr.Files(label="Downloadable output file", scale=3)
-                        btn_openfolder = gr.Button('📂', scale=1)
+        btn_run.click(fn=self.transcribe_file_wrapper,
+                      inputs=params + whisper_params.to_list(),
+                      outputs=[tb_indicator, files_subtitles])
+        btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
+        dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])
 
-                    params = [mic_input, dd_file_format]
-                    whisper_params = WhisperGradioComponents(model_size=dd_model,
-                                                             lang=dd_lang,
-                                                             is_translate=cb_translate,
-                                                             beam_size=nb_beam_size,
-                                                             log_prob_threshold=nb_log_prob_threshold,
-                                                             no_speech_threshold=nb_no_speech_threshold,
-                                                             compute_type=dd_compute_type,
-                                                             best_of=nb_best_of,
-                                                             patience=nb_patience,
-                                                             condition_on_previous_text=cb_condition_on_previous_text,
-                                                             initial_prompt=tb_initial_prompt,
-                                                             temperature=sd_temperature,
-                                                             compression_ratio_threshold=nb_compression_ratio_threshold,
-                                                             vad_filter=cb_vad_filter,
-                                                             threshold=sd_threshold,
-                                                             min_speech_duration_ms=nb_min_speech_duration_ms,
-                                                             max_speech_duration_s=nb_max_speech_duration_s,
-                                                             min_silence_duration_ms=nb_min_silence_duration_ms,
-                                                             window_size_sample=nb_window_size_sample,
-                                                             speech_pad_ms=nb_speech_pad_ms)
-                    btn_run.click(fn=self.whisper_inf.transcribe_mic,
-                                  inputs=params + whisper_params.to_list(),
-                                  outputs=[tb_indicator, files_subtitles])
-                    btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
-                    dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])
+    def build_youtube_tab(self):
+        with gr.Row():
+            tb_youtubelink = gr.Textbox(label="Youtube Link")
+        with gr.Row(equal_height=True):
+            with gr.Column():
+                img_thumbnail = gr.Image(label="Youtube Thumbnail")
+            with gr.Column():
+                tb_title = gr.Label(label="Youtube Title")
+                tb_description = gr.Textbox(label="Youtube Description", max_lines=15)
+        with gr.Row():
+            dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-v2", label="Model")
+            dd_lang = gr.Dropdown(choices=["Automatic Detection"] + self.whisper_inf.available_langs, value="Automatic Detection", label="Language")
+            dd_file_format = gr.Dropdown(choices=["SRT", "WebVTT", "txt"], value="SRT", label="File Format")
+        with gr.Row():
+            cb_translate = gr.Checkbox(value=False, label="Translate to English?", interactive=True)
+        with gr.Row():
+            cb_timestamp = gr.Checkbox(value=True, label="Add a timestamp to the end of the filename", interactive=True)
+        with gr.Accordion("VAD Options", open=False, visible=isinstance(self.whisper_inf, FasterWhisperInference)):
+            cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
+            sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5)
+            nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250)
+            nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999)
+            nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000)
+            nb_window_size_sample = gr.Number(label="Window Size (samples)", precision=0, value=1024)
+            nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
+        with gr.Accordion("Advanced_Parameters", open=False):
+            nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
+            nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
+            nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
+            dd_compute_type = gr.Dropdown(choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, label="Compute Type", interactive=True)
+            nb_best_of = gr.Number(label="Best Of", value=5, interactive=True)
+            nb_patience = gr.Number(label="Patience", value=1, interactive=True)
+            cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True)
+            tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True)
+            sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True)
+            nb_compression_ratio_threshold = gr.Number(label="Compression Ratio Threshold", value=2.4, interactive=True)
+        with gr.Row():
+            btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
+        with gr.Row():
+            tb_indicator = gr.Textbox(label="Output", scale=5)
+            files_subtitles = gr.Files(label="Downloadable output file", scale=3)
+            btn_openfolder = gr.Button('📂', scale=1)
 
-                with gr.TabItem("T2T Translation"):  # tab 4
-                    with gr.Row():
-                        file_subs = gr.Files(type="filepath", label="Upload Subtitle Files to translate here",
-                                             file_types=['.vtt', '.srt'])
+        params = [tb_youtubelink, dd_file_format, cb_timestamp]
+        whisper_params = WhisperGradioComponents(model_size=dd_model,
+                                                 lang=dd_lang,
+                                                 is_translate=cb_translate,
+                                                 beam_size=nb_beam_size,
+                                                 log_prob_threshold=nb_log_prob_threshold,
+                                                 no_speech_threshold=nb_no_speech_threshold,
+                                                 compute_type=dd_compute_type,
+                                                 best_of=nb_best_of,
+                                                 patience=nb_patience,
+                                                 condition_on_previous_text=cb_condition_on_previous_text,
+                                                 initial_prompt=tb_initial_prompt,
+                                                 temperature=sd_temperature,
+                                                 compression_ratio_threshold=nb_compression_ratio_threshold,
+                                                 vad_filter=cb_vad_filter,
+                                                 threshold=sd_threshold,
+                                                 min_speech_duration_ms=nb_min_speech_duration_ms,
+                                                 max_speech_duration_s=nb_max_speech_duration_s,
+                                                 min_silence_duration_ms=nb_min_silence_duration_ms,
+                                                 window_size_sample=nb_window_size_sample,
+                                                 speech_pad_ms=nb_speech_pad_ms)
 
-                    with gr.TabItem("DeepL API"):  # sub tab1
-                        with gr.Row():
-                            tb_authkey = gr.Textbox(label="Your Auth Key (API KEY)",
-                                                    value="")
-                        with gr.Row():
-                            dd_deepl_sourcelang = gr.Dropdown(label="Source Language", value="Automatic Detection",
-                                                              choices=list(
-                                                                  self.deepl_api.available_source_langs.keys()))
-                            dd_deepl_targetlang = gr.Dropdown(label="Target Language", value="English",
-                                                              choices=list(
-                                                                  self.deepl_api.available_target_langs.keys()))
-                        with gr.Row():
-                            cb_deepl_ispro = gr.Checkbox(label="Pro User?", value=False)
-                        with gr.Row():
-                            btn_run = gr.Button("TRANSLATE SUBTITLE FILE", variant="primary")
-                        with gr.Row():
-                            tb_indicator = gr.Textbox(label="Output", scale=5)
-                            files_subtitles = gr.Files(label="Downloadable output file", scale=3)
-                            btn_openfolder = gr.Button('📂', scale=1)
+        btn_run.click(fn=self.whisper_inf.transcribe_youtube,
+                      inputs=params + whisper_params.to_list(),
+                      outputs=[tb_indicator, files_subtitles])
+        tb_youtubelink.change(get_ytmetas, inputs=[tb_youtubelink], outputs=[img_thumbnail, tb_title, tb_description])
+        btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
+        dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])
 
-                    btn_run.click(fn=self.deepl_api.translate_deepl,
-                                  inputs=[tb_authkey, file_subs, dd_deepl_sourcelang, dd_deepl_targetlang,
-                                          cb_deepl_ispro],
-                                  outputs=[tb_indicator, files_subtitles])
+    def build_mic_tab(self):
+        with gr.Row():
+            mic_input = gr.Microphone(label="Record with Mic", type="filepath", interactive=True)
+        with gr.Row():
+            dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-v2", label="Model")
+            dd_lang = gr.Dropdown(choices=["Automatic Detection"] + self.whisper_inf.available_langs, value="Automatic Detection", label="Language")
+            dd_file_format = gr.Dropdown(choices=["SRT", "WebVTT", "txt"], value="SRT", label="File Format")
+        with gr.Row():
+            cb_translate = gr.Checkbox(value=False, label="Translate to English?", interactive=True)
+        with gr.Accordion("VAD Options", open=False, visible=isinstance(self.whisper_inf, FasterWhisperInference)):
+            cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
+            sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5)
+            nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250)
+            nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999)
+            nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000)
+            nb_window_size_sample = gr.Number(label="Window Size (samples)", precision=0, value=1024)
+            nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
+        with gr.Accordion("Advanced_Parameters", open=False):
+            nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
+            nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
+            nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
+            dd_compute_type = gr.Dropdown(choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, label="Compute Type", interactive=True)
+            nb_best_of = gr.Number(label="Best Of", value=5, interactive=True)
+            nb_patience = gr.Number(label="Patience", value=1, interactive=True)
+            cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True)
+            tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True)
+            sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True)
+        with gr.Row():
+            btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
+        with gr.Row():
+            tb_indicator = gr.Textbox(label="Output", scale=5)
+            files_subtitles = gr.Files(label="Downloadable output file", scale=3)
+            btn_openfolder = gr.Button('📂', scale=1)
 
-                    btn_openfolder.click(fn=lambda: self.open_folder(os.path.join("outputs", "translations")),
-                                         inputs=None,
-                                         outputs=None)
+        params = [mic_input, dd_file_format]
+        whisper_params = WhisperGradioComponents(model_size=dd_model,
+                                                 lang=dd_lang,
+                                                 is_translate=cb_translate,
+                                                 beam_size=nb_beam_size,
+                                                 log_prob_threshold=nb_log_prob_threshold,
+                                                 no_speech_threshold=nb_no_speech_threshold,
+                                                 compute_type=dd_compute_type,
+                                                 best_of=nb_best_of,
+                                                 patience=nb_patience,
+                                                 condition_on_previous_text=cb_condition_on_previous_text,
+                                                 initial_prompt=tb_initial_prompt,
+                                                 temperature=sd_temperature,
+                                                 compression_ratio_threshold=nb_compression_ratio_threshold,
+                                                 vad_filter=cb_vad_filter,
+                                                 threshold=sd_threshold,
+                                                 min_speech_duration_ms=nb_min_speech_duration_ms,
+                                                 max_speech_duration_s=nb_max_speech_duration_s,
+                                                 min_silence_duration_ms=nb_min_silence_duration_ms,
+                                                 window_size_sample=nb_window_size_sample,
+                                                 speech_pad_ms=nb_speech_pad_ms)
 
-                    with gr.TabItem("NLLB"):  # sub tab2
-                        with gr.Row():
-                            dd_nllb_model = gr.Dropdown(label="Model", value="facebook/nllb-200-1.3B",
-                                                        choices=self.nllb_inf.available_models)
-                            dd_nllb_sourcelang = gr.Dropdown(label="Source Language",
-                                                             choices=self.nllb_inf.available_source_langs)
-                            dd_nllb_targetlang = gr.Dropdown(label="Target Language",
-                                                             choices=self.nllb_inf.available_target_langs)
-                        with gr.Row():
-                            cb_timestamp = gr.Checkbox(value=True, label="Add a timestamp to the end of the filename", interactive=True)
-                        with gr.Row():
-                            btn_run = gr.Button("TRANSLATE SUBTITLE FILE", variant="primary")
-                        with gr.Row():
-                            tb_indicator = gr.Textbox(label="Output", scale=5)
-                            files_subtitles = gr.Files(label="Downloadable output file", scale=3)
-                            btn_openfolder = gr.Button('📂', scale=1)
-                        with gr.Column():
-                            md_vram_table = gr.HTML(NLLB_VRAM_TABLE, elem_id="md_nllb_vram_table")
+        btn_run.click(fn=self.whisper_inf.transcribe_mic,
+                      inputs=params + whisper_params.to_list(),
+                      outputs=[tb_indicator, files_subtitles])
+        btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
+        dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])
 
-                    btn_run.click(fn=self.nllb_inf.translate_file,
-                                  inputs=[file_subs, dd_nllb_model, dd_nllb_sourcelang, dd_nllb_targetlang, cb_timestamp],
-                                  outputs=[tb_indicator, files_subtitles])
+    def build_t2t_translation_tab(self):
+        with gr.Row():
+            file_subs = gr.Files(type="filepath", label="Upload Subtitle Files to translate here", file_types=['.vtt', '.srt'])
 
-                    btn_openfolder.click(fn=lambda: self.open_folder(os.path.join("outputs", "translations")),
-                                         inputs=None,
-                                         outputs=None)
+        with gr.TabItem("DeepL API"):
+            with gr.Row():
+                tb_authkey = gr.Textbox(label="Your Auth Key (API KEY)")
+            with gr.Row():
+                dd_deepl_sourcelang = gr.Dropdown(label="Source Language", value="Automatic Detection", choices=list(self.deepl_api.available_source_langs.keys()))
+                dd_deepl_targetlang = gr.Dropdown(label="Target Language", value="English", choices=list(self.deepl_api.available_target_langs.keys()))
+            with gr.Row():
+                cb_deepl_ispro = gr.Checkbox(label="Pro User?", value=False)
+            with gr.Row():
+                btn_run = gr.Button("TRANSLATE SUBTITLE FILE", variant="primary")
+            with gr.Row():
+                tb_indicator = gr.Textbox(label
+                                          ="Output", scale=5)
+                files_subtitles = gr.Files(label="Downloadable output file", scale=3)
+                btn_openfolder = gr.Button('📂', scale=1)
 
-        # Launch the app with optional gradio settings
-        launch_args = {}
-        if self.args.share:
-            launch_args['share'] = self.args.share
-        if self.args.server_name:
-            launch_args['server_name'] = self.args.server_name
-        if self.args.server_port:
-            launch_args['server_port'] = self.args.server_port
-        if self.args.username and self.args.password:
-            launch_args['auth'] = (self.args.username, self.args.password)
-        launch_args['inbrowser'] = True
+            btn_run.click(fn=self.deepl_api.translate_deepl,
+                          inputs=[tb_authkey, file_subs, dd_deepl_sourcelang, dd_deepl_targetlang, cb_deepl_ispro],
+                          outputs=[tb_indicator, files_subtitles])
+
+            btn_openfolder.click(fn=lambda: self.open_folder(os.path.join("outputs", "translations")),
+                                 inputs=None,
+                                 outputs=None)
+
+        with gr.TabItem("NLLB"):
+            with gr.Row():
+                dd_nllb_model = gr.Dropdown(label="Model", value="facebook/nllb-200-1.3B", choices=self.nllb_inf.available_models)
+                dd_nllb_sourcelang = gr.Dropdown(label="Source Language", choices=self.nllb_inf.available_source_langs)
+                dd_nllb_targetlang = gr.Dropdown(label="Target Language", choices=self.nllb_inf.available_target_langs)
+            with gr.Row():
+                cb_timestamp = gr.Checkbox(value=True, label="Add a timestamp to the end of the filename", interactive=True)
+            with gr.Row():
+                btn_run = gr.Button("TRANSLATE SUBTITLE FILE", variant="primary")
+            with gr.Row():
+                tb_indicator = gr.Textbox(label="Output", scale=5)
+                files_subtitles = gr.Files(label="Downloadable output file", scale=3)
+                btn_openfolder = gr.Button('📂', scale=1)
+            with gr.Column():
+                md_vram_table = gr.HTML(NLLB_VRAM_TABLE, elem_id="md_nllb_vram_table")
+
+            btn_run.click(fn=self.nllb_inf.translate_file,
+                          inputs=[file_subs, dd_nllb_model, dd_nllb_sourcelang, dd_nllb_targetlang, cb_timestamp],
+                          outputs=[tb_indicator, files_subtitles])
 
-        self.app.queue(api_open=False).launch(**launch_args)
+            btn_openfolder.click(fn=lambda: self.open_folder(os.path.join("outputs", "translations")),
+                                 inputs=None,
+                                 outputs=None)
 
 
 # Create the parser for command-line arguments
@@ -356,10 +349,11 @@ parser.add_argument('--username', type=str, default=None, help='Gradio authentic
 parser.add_argument('--password', type=str, default=None, help='Gradio authentication password')
 parser.add_argument('--theme', type=str, default=None, help='Gradio Blocks theme')
 parser.add_argument('--colab', type=bool, default=False, nargs='?', const=True, help='Is colab user or not')
-parser.add_argument('--api_open', type=bool, default=False, nargs='?', const=True, help='enable api or not')
+parser.add_argument('--api_open', type=bool, default=False, nargs='?', const=True, help='enable API or not')
 parser.add_argument('--whisper_model_dir', type=str, default=os.path.join("models", "Whisper"), help='Directory path of the whisper model')
 parser.add_argument('--faster_whisper_model_dir', type=str, default=os.path.join("models", "Whisper", "faster-whisper"), help='Directory path of the faster-whisper model')
 _args = parser.parse_args()
 
 if __name__ == "__main__":
-    app = App(args=_args)
+    app = App(args=_args)
+    app.launch()