refactor parameters to function

app.py

@@ -60,6 +60,112 @@ class App:
             )
         return whisper_inf
 
+    def create_whisper_parameters(self):
+        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("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.Group(visible=isinstance(self.whisper_inf, FasterWhisperInference)):
+                nb_length_penalty = gr.Number(label="Length Penalty", value=1,
+                                              info="Exponential length penalty constant.")
+                nb_repetition_penalty = gr.Number(label="Repetition Penalty", value=1,
+                                                  info="Penalty applied to the score of previously generated tokens (set > 1 to penalize).")
+                nb_no_repeat_ngram_size = gr.Number(label="No Repeat N-gram Size", value=0, precision=0,
+                                                    info="Prevent repetitions of n-grams with this size (set 0 to disable).")
+                tb_prefix = gr.Textbox(label="Prefix", value=lambda: None,
+                                       info="Optional text to provide as a prefix for the first window.")
+                cb_suppress_blank = gr.Checkbox(label="Suppress Blank", value=True,
+                                                info="Suppress blank outputs at the beginning of the sampling.")
+                tb_suppress_tokens = gr.Textbox(label="Suppress Tokens", value="[-1]",
+                                                info="List of token IDs to suppress. -1 will suppress a default set of symbols as defined in the model config.json file.")
+                nb_max_initial_timestamp = gr.Number(label="Max Initial Timestamp", value=1.0,
+                                                     info="The initial timestamp cannot be later than this.")
+                cb_word_timestamps = gr.Checkbox(label="Word Timestamps", value=False,
+                                                 info="Extract word-level timestamps using the cross-attention pattern and dynamic time warping, and include the timestamps for each word in each segment.")
+                tb_prepend_punctuations = gr.Textbox(label="Prepend Punctuations", value="\"'“¿([{-",
+                                                     info="If 'Word Timestamps' is True, merge these punctuation symbols with the next word.")
+                tb_append_punctuations = gr.Textbox(label="Append Punctuations", value="\"'.。,，!！?？:：”)]}、",
+                                                    info="If 'Word Timestamps' is True, merge these punctuation symbols with the previous word.")
+                nb_max_new_tokens = gr.Number(label="Max New Tokens", value=lambda: None, precision=0,
+                                              info="Maximum number of new tokens to generate per-chunk. If not set, the maximum will be set by the default max_length.")
+                nb_chunk_length = gr.Number(label="Chunk Length", value=lambda: None, precision=0,
+                                            info="The length of audio segments. If it is not None, it will overwrite the default chunk_length of the FeatureExtractor.")
+                nb_hallucination_silence_threshold = gr.Number(label="Hallucination Silence Threshold (sec)",
+                                                               value=lambda: None,
+                                                               info="When 'Word Timestamps' is True, skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected.")
+                tb_hotwords = gr.Textbox(label="Hotwords", value=None,
+                                         info="Hotwords/hint phrases to provide the model with. Has no effect if prefix is not None.")
+                nb_language_detection_threshold = gr.Number(label="Language Detection Threshold", value=None,
+                                                            info="If the maximum probability of the language tokens is higher than this value, the language is detected.")
+                nb_language_detection_segments = gr.Number(label="Language Detection Segments", value=1, precision=0,
+                                                           info="Number of segments to consider for the language detection.")
+            with gr.Group(visible=isinstance(self.whisper_inf, InsanelyFastWhisperInference)):
+                nb_chunk_length_s = gr.Number(label="Chunk Lengths (sec)", value=30, precision=0)
+                nb_batch_size = gr.Number(label="Batch Size", value=24, precision=0)
+
+        with gr.Accordion("VAD", open=False):
+            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,
+                                     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=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_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
+
+        with gr.Accordion("Diarization", open=False):
+            cb_diarize = gr.Checkbox(label="Enable Diarization")
+            tb_hf_token = gr.Text(label="HuggingFace Token", value="",
+                                  info="This is only needed the first time you download the model. If you already have models, you don't need to enter. To download the model, you must manually go to \"https://huggingface.co/pyannote/speaker-diarization-3.1\" and agree to their requirement.")
+            dd_diarization_device = gr.Dropdown(label="Device",
+                                                choices=self.whisper_inf.diarizer.get_available_device(),
+                                                value=self.whisper_inf.diarizer.get_device())
+
+        dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])
+
+        return (
+            WhisperParameters(
+                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,
+                speech_pad_ms=nb_speech_pad_ms, chunk_length_s=nb_chunk_length_s, batch_size=nb_batch_size,
+                is_diarize=cb_diarize, hf_token=tb_hf_token, diarization_device=dd_diarization_device,
+                length_penalty=nb_length_penalty, repetition_penalty=nb_repetition_penalty,
+                no_repeat_ngram_size=nb_no_repeat_ngram_size, prefix=tb_prefix, suppress_blank=cb_suppress_blank,
+                suppress_tokens=tb_suppress_tokens, max_initial_timestamp=nb_max_initial_timestamp,
+                word_timestamps=cb_word_timestamps, prepend_punctuations=tb_prepend_punctuations,
+                append_punctuations=tb_append_punctuations, max_new_tokens=nb_max_new_tokens, chunk_length=nb_chunk_length,
+                hallucination_silence_threshold=nb_hallucination_silence_threshold, hotwords=tb_hotwords,
+                language_detection_threshold=nb_language_detection_threshold,
+                language_detection_segments=nb_language_detection_segments
+            ),
+            dd_file_format,
+            cb_timestamp
+        )
+
     def launch(self):
         with self.app:
             with gr.Row():
@@ -74,94 +180,9 @@ class App:
                                                           " Leave this field empty if you do not wish to use a local path.",
                                                      visible=self.args.colab,
                                                      value="")
-                    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("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.Group(visible=isinstance(self.whisper_inf, FasterWhisperInference)):
-                            with gr.Column():
-                                nb_length_penalty = gr.Number(label="Length Penalty", value=1,
-                                                              info="Exponential length penalty constant.")
-                                nb_repetition_penalty = gr.Number(label="Repetition Penalty", value=1,
-                                                                  info="Penalty applied to the score of previously generated tokens (set > 1 to penalize).")
-                                nb_no_repeat_ngram_size = gr.Number(label="No Repeat N-gram Size", value=0, precision=0,
-                                                                    info="Prevent repetitions of n-grams with this size (set 0 to disable).")
-                                tb_prefix = gr.Textbox(label="Prefix", value=lambda: None,  # Bug Fix https://github.com/gradio-app/gradio/issues/6728
-                                                       info="Optional text to provide as a prefix for the first window.")
-                                cb_suppress_blank = gr.Checkbox(label="Suppress Blank", value=True,
-                                                                info="Suppress blank outputs at the beginning of the sampling.")
-                                tb_suppress_tokens = gr.Textbox(label="Suppress Tokens", value="[-1]",
-                                                                info="List of token IDs to suppress. -1 will suppress a default set of symbols as defined in the model config.json file.")
-                                nb_max_initial_timestamp = gr.Number(label="Max Initial Timestamp", value=1.0,
-                                                                     info="The initial timestamp cannot be later than this.")
-                                cb_word_timestamps = gr.Checkbox(label="Word Timestamps", value=False,
-                                                                 info="Extract word-level timestamps using the cross-attention pattern and dynamic time warping, and include the timestamps for each word in each segment.")
-                                tb_prepend_punctuations = gr.Textbox(label="Prepend Punctuations", value="\"'“¿([{-",
-                                                                     info="If 'Word Timestamps' is True, merge these punctuation symbols with the next word.")
-                                tb_append_punctuations = gr.Textbox(label="Append Punctuations",
-                                                                    value="\"'.。,，!！?？:：”)]}、",
-                                                                    info="If 'Word Timestamps' is True, merge these punctuation symbols with the previous word.")
-                                nb_max_new_tokens = gr.Number(label="Max New Tokens", value=lambda: None, precision=0,
-                                                              info="Maximum number of new tokens to generate per-chunk. If not set, the maximum will be set by the default max_length.")
-                                nb_chunk_length = gr.Number(label="Chunk Length", value=lambda: None, precision=0,
-                                                            info="The length of audio segments. If it is not None, it will overwrite the default chunk_length of the FeatureExtractor.")
-                                nb_hallucination_silence_threshold = gr.Number(label="Hallucination Silence Threshold (sec)",
-                                                                               value=lambda: None,
-                                                                               info="When 'Word Timestamps' is True, skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected.")
-                                tb_hotwords = gr.Textbox(label="Hotwords", value=None,
-                                                         info="Hotwords/hint phrases to provide the model with. Has no effect if prefix is not None.")
-                                nb_language_detection_threshold = gr.Number(label="Language Detection Threshold",
-                                                                            value=None,
-                                                                            info="If the maximum probability of the language tokens is higher than this value, the language is detected.")
-                                nb_language_detection_segments = gr.Number(label="Language Detection Segments", value=1,
-                                                                           precision=0,
-                                                                           info="Number of segments to consider for the language detection.")
-
-                        with gr.Group(visible=isinstance(self.whisper_inf, InsanelyFastWhisperInference)):
-                            nb_chunk_length_s = gr.Number(label="Chunk Lengths (sec)", value=30, precision=0)
-                            nb_batch_size = gr.Number(label="Batch Size", value=24, precision=0)
-                    with gr.Accordion("VAD", open=False):
-                        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, 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=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_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
-                    with gr.Accordion("Diarization", open=False):
-                        cb_diarize = gr.Checkbox(label="Enable Diarization")
-                        tb_hf_token = gr.Text(label="HuggingFace Token", value="",
-                                              info="This is only needed the first time you download the model. If you already have models, you don't need to enter. "
-                                                   "To download the model, you must manually go to \"https://huggingface.co/pyannote/speaker-diarization-3.1\" and agree to their requirement.")
-                        dd_diarization_device = gr.Dropdown(label="Device",
-                                                            choices=self.whisper_inf.diarizer.get_available_device(),
-                                                            value=self.whisper_inf.diarizer.get_device())
+
+                    whisper_params, dd_file_format, cb_timestamp = self.create_whisper_parameters()
+
                     with gr.Row():
                         btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
                     with gr.Row():
@@ -170,54 +191,10 @@ class App:
                         btn_openfolder = gr.Button('📂', scale=1)
 
                     params = [input_file, tb_input_folder, dd_file_format, cb_timestamp]
-                    whisper_params = WhisperParameters(
-                        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,
-                        speech_pad_ms=nb_speech_pad_ms,
-                        chunk_length_s=nb_chunk_length_s,
-                        batch_size=nb_batch_size,
-                        is_diarize=cb_diarize,
-                        hf_token=tb_hf_token,
-                        diarization_device=dd_diarization_device,
-                        length_penalty=nb_length_penalty,
-                        repetition_penalty=nb_repetition_penalty,
-                        no_repeat_ngram_size=nb_no_repeat_ngram_size,
-                        prefix=tb_prefix,
-                        suppress_blank=cb_suppress_blank,
-                        suppress_tokens=tb_suppress_tokens,
-                        max_initial_timestamp=nb_max_initial_timestamp,
-                        word_timestamps=cb_word_timestamps,
-                        prepend_punctuations=tb_prepend_punctuations,
-                        append_punctuations=tb_append_punctuations,
-                        max_new_tokens=nb_max_new_tokens,
-                        chunk_length=nb_chunk_length,
-                        hallucination_silence_threshold=nb_hallucination_silence_threshold,
-                        hotwords=tb_hotwords,
-                        language_detection_threshold=nb_language_detection_threshold,
-                        language_detection_segments=nb_language_detection_segments
-                    )
-
                     btn_run.click(fn=self.whisper_inf.transcribe_file,
                                   inputs=params + whisper_params.as_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])
 
                 with gr.TabItem("Youtube"):  # tab2
                     with gr.Row():
@@ -228,95 +205,9 @@ class App:
                         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("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.Group(visible=isinstance(self.whisper_inf, FasterWhisperInference)):
-                            with gr.Column():
-                                nb_length_penalty = gr.Number(label="Length Penalty", value=1,
-                                                              info="Exponential length penalty constant.")
-                                nb_repetition_penalty = gr.Number(label="Repetition Penalty", value=1,
-                                                                  info="Penalty applied to the score of previously generated tokens (set > 1 to penalize).")
-                                nb_no_repeat_ngram_size = gr.Number(label="No Repeat N-gram Size", value=0, precision=0,
-                                                                    info="Prevent repetitions of n-grams with this size (set 0 to disable).")
-                                tb_prefix = gr.Textbox(label="Prefix", value=lambda: None, # Bug Fix https://github.com/gradio-app/gradio/issues/6728
-                                                       info="Optional text to provide as a prefix for the first window.")
-                                cb_suppress_blank = gr.Checkbox(label="Suppress Blank", value=True,
-                                                                info="Suppress blank outputs at the beginning of the sampling.")
-                                tb_suppress_tokens = gr.Textbox(label="Suppress Tokens", value="[-1]",
-                                                                info="List of token IDs to suppress. -1 will suppress a default set of symbols as defined in the model config.json file.")
-                                nb_max_initial_timestamp = gr.Number(label="Max Initial Timestamp", value=1.0,
-                                                                     info="The initial timestamp cannot be later than this.")
-                                cb_word_timestamps = gr.Checkbox(label="Word Timestamps", value=False,
-                                                                 info="Extract word-level timestamps using the cross-attention pattern and dynamic time warping, and include the timestamps for each word in each segment.")
-                                tb_prepend_punctuations = gr.Textbox(label="Prepend Punctuations", value="\"'“¿([{-",
-                                                                     info="If 'Word Timestamps' is True, merge these punctuation symbols with the next word.")
-                                tb_append_punctuations = gr.Textbox(label="Append Punctuations",
-                                                                    value="\"'.。,，!！?？:：”)]}、",
-                                                                    info="If 'Word Timestamps' is True, merge these punctuation symbols with the previous word.")
-                                nb_max_new_tokens = gr.Number(label="Max New Tokens", value=lambda: None, precision=0,
-                                                              info="Maximum number of new tokens to generate per-chunk. If not set, the maximum will be set by the default max_length.")
-                                nb_chunk_length = gr.Number(label="Chunk Length", value=lambda: None, precision=0,
-                                                            info="The length of audio segments. If it is not None, it will overwrite the default chunk_length of the FeatureExtractor.")
-                                nb_hallucination_silence_threshold = gr.Number(
-                                    label="Hallucination Silence Threshold (sec)",
-                                    value=lambda: None,
-                                    info="When 'Word Timestamps' is True, skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected.")
-                                tb_hotwords = gr.Textbox(label="Hotwords", value=None,
-                                                         info="Hotwords/hint phrases to provide the model with. Has no effect if prefix is not None.")
-                                nb_language_detection_threshold = gr.Number(label="Language Detection Threshold",
-                                                                            value=None,
-                                                                            info="If the maximum probability of the language tokens is higher than this value, the language is detected.")
-                                nb_language_detection_segments = gr.Number(label="Language Detection Segments", value=1,
-                                                                           precision=0,
-                                                                           info="Number of segments to consider for the language detection.")
-
-                        with gr.Group(visible=isinstance(self.whisper_inf, InsanelyFastWhisperInference)):
-                            nb_chunk_length_s = gr.Number(label="Chunk Lengths (sec)", value=30, precision=0)
-                            nb_batch_size = gr.Number(label="Batch Size", value=24, precision=0)
-                    with gr.Accordion("VAD", open=False):
-                        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, 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=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_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
-                    with gr.Accordion("Diarization", open=False):
-                        cb_diarize = gr.Checkbox(label="Enable Diarization")
-                        tb_hf_token = gr.Text(label="HuggingFace Token", value="",
-                                              info="This is only needed the first time you download the model. If you already have models, you don't need to enter. "
-                                                   "To download the model, you must manually go to \"https://huggingface.co/pyannote/speaker-diarization-3.1\" and agree to their requirement.")
-                        dd_diarization_device = gr.Dropdown(label="Device",
-                                                            choices=self.whisper_inf.diarizer.get_available_device(),
-                                                            value=self.whisper_inf.diarizer.get_device())
+
+                    whisper_params, dd_file_format, cb_timestamp = self.create_whisper_parameters()
+
                     with gr.Row():
                         btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
                     with gr.Row():
@@ -325,48 +216,6 @@ class App:
                         btn_openfolder = gr.Button('📂', scale=1)
 
                     params = [tb_youtubelink, dd_file_format, cb_timestamp]
-                    whisper_params = WhisperParameters(
-                        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,
-                        speech_pad_ms=nb_speech_pad_ms,
-                        chunk_length_s=nb_chunk_length_s,
-                        batch_size=nb_batch_size,
-                        is_diarize=cb_diarize,
-                        hf_token=tb_hf_token,
-                        diarization_device=dd_diarization_device,
-                        length_penalty=nb_length_penalty,
-                        repetition_penalty=nb_repetition_penalty,
-                        no_repeat_ngram_size=nb_no_repeat_ngram_size,
-                        prefix=tb_prefix,
-                        suppress_blank=cb_suppress_blank,
-                        suppress_tokens=tb_suppress_tokens,
-                        max_initial_timestamp=nb_max_initial_timestamp,
-                        word_timestamps=cb_word_timestamps,
-                        prepend_punctuations=tb_prepend_punctuations,
-                        append_punctuations=tb_append_punctuations,
-                        max_new_tokens=nb_max_new_tokens,
-                        chunk_length=nb_chunk_length,
-                        hallucination_silence_threshold=nb_hallucination_silence_threshold,
-                        hotwords=tb_hotwords,
-                        language_detection_threshold=nb_language_detection_threshold,
-                        language_detection_segments=nb_language_detection_segments
-                    )
 
                     btn_run.click(fn=self.whisper_inf.transcribe_youtube,
                                   inputs=params + whisper_params.as_list(),
@@ -374,97 +223,13 @@ class App:
                     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])
 
                 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("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.Group(visible=isinstance(self.whisper_inf, FasterWhisperInference)):
-                            with gr.Column():
-                                nb_length_penalty = gr.Number(label="Length Penalty", value=1,
-                                                              info="Exponential length penalty constant.")
-                                nb_repetition_penalty = gr.Number(label="Repetition Penalty", value=1,
-                                                                  info="Penalty applied to the score of previously generated tokens (set > 1 to penalize).")
-                                nb_no_repeat_ngram_size = gr.Number(label="No Repeat N-gram Size", value=0, precision=0,
-                                                                    info="Prevent repetitions of n-grams with this size (set 0 to disable).")
-                                tb_prefix = gr.Textbox(label="Prefix", value=lambda: None,  # Bug Fix https://github.com/gradio-app/gradio/issues/6728
-                                                       info="Optional text to provide as a prefix for the first window.")
-                                cb_suppress_blank = gr.Checkbox(label="Suppress Blank", value=True,
-                                                                info="Suppress blank outputs at the beginning of the sampling.")
-                                tb_suppress_tokens = gr.Textbox(label="Suppress Tokens", value="[-1]",
-                                                                info="List of token IDs to suppress. -1 will suppress a default set of symbols as defined in the model config.json file.")
-                                nb_max_initial_timestamp = gr.Number(label="Max Initial Timestamp", value=1.0,
-                                                                     info="The initial timestamp cannot be later than this.")
-                                cb_word_timestamps = gr.Checkbox(label="Word Timestamps", value=False,
-                                                                 info="Extract word-level timestamps using the cross-attention pattern and dynamic time warping, and include the timestamps for each word in each segment.")
-                                tb_prepend_punctuations = gr.Textbox(label="Prepend Punctuations", value="\"'“¿([{-",
-                                                                     info="If 'Word Timestamps' is True, merge these punctuation symbols with the next word.")
-                                tb_append_punctuations = gr.Textbox(label="Append Punctuations",
-                                                                    value="\"'.。,，!！?？:：”)]}、",
-                                                                    info="If 'Word Timestamps' is True, merge these punctuation symbols with the previous word.")
-                                nb_max_new_tokens = gr.Number(label="Max New Tokens", value=lambda: None, precision=0,
-                                                              info="Maximum number of new tokens to generate per-chunk. If not set, the maximum will be set by the default max_length.")
-                                nb_chunk_length = gr.Number(label="Chunk Length", value=lambda: None, precision=0,
-                                                            info="The length of audio segments. If it is not None, it will overwrite the default chunk_length of the FeatureExtractor.")
-                                nb_hallucination_silence_threshold = gr.Number(
-                                    label="Hallucination Silence Threshold (sec)",
-                                    value=lambda: None,
-                                    info="When 'Word Timestamps' is True, skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected.")
-                                tb_hotwords = gr.Textbox(label="Hotwords", value=None,
-                                                         info="Hotwords/hint phrases to provide the model with. Has no effect if prefix is not None.")
-                                nb_language_detection_threshold = gr.Number(label="Language Detection Threshold",
-                                                                            value=None,
-                                                                            info="If the maximum probability of the language tokens is higher than this value, the language is detected.")
-                                nb_language_detection_segments = gr.Number(label="Language Detection Segments", value=1,
-                                                                           precision=0,
-                                                                           info="Number of segments to consider for the language detection.")
-
-                        with gr.Group(visible=isinstance(self.whisper_inf, InsanelyFastWhisperInference)):
-                            nb_chunk_length_s = gr.Number(label="Chunk Lengths (sec)", value=30, precision=0)
-                            nb_batch_size = gr.Number(label="Batch Size", value=24, precision=0)
-
-                    with gr.Accordion("VAD", open=False):
-                        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, 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=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_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
-                    with gr.Accordion("Diarization", open=False):
-                        cb_diarize = gr.Checkbox(label="Enable Diarization")
-                        tb_hf_token = gr.Text(label="HuggingFace Token", value="",
-                                              info="This is only needed the first time you download the model. If you already have models, you don't need to enter. "
-                                                   "To download the model, you must manually go to \"https://huggingface.co/pyannote/speaker-diarization-3.1\" and agree to their requirement.")
-                        dd_diarization_device = gr.Dropdown(label="Device",
-                                                            choices=self.whisper_inf.diarizer.get_available_device(),
-                                                            value=self.whisper_inf.diarizer.get_device())
+
+                    whisper_params, dd_file_format, cb_timestamp = self.create_whisper_parameters()
+
                     with gr.Row():
                         btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
                     with gr.Row():
@@ -473,54 +238,11 @@ class App:
                         btn_openfolder = gr.Button('📂', scale=1)
 
                     params = [mic_input, dd_file_format]
-                    whisper_params = WhisperParameters(
-                        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,
-                        speech_pad_ms=nb_speech_pad_ms,
-                        chunk_length_s=nb_chunk_length_s,
-                        batch_size=nb_batch_size,
-                        is_diarize=cb_diarize,
-                        hf_token=tb_hf_token,
-                        diarization_device=dd_diarization_device,
-                        length_penalty=nb_length_penalty,
-                        repetition_penalty=nb_repetition_penalty,
-                        no_repeat_ngram_size=nb_no_repeat_ngram_size,
-                        prefix=tb_prefix,
-                        suppress_blank=cb_suppress_blank,
-                        suppress_tokens=tb_suppress_tokens,
-                        max_initial_timestamp=nb_max_initial_timestamp,
-                        word_timestamps=cb_word_timestamps,
-                        prepend_punctuations=tb_prepend_punctuations,
-                        append_punctuations=tb_append_punctuations,
-                        max_new_tokens=nb_max_new_tokens,
-                        chunk_length=nb_chunk_length,
-                        hallucination_silence_threshold=nb_hallucination_silence_threshold,
-                        hotwords=tb_hotwords,
-                        language_detection_threshold=nb_language_detection_threshold,
-                        language_detection_segments=nb_language_detection_segments
-                    )
 
                     btn_run.click(fn=self.whisper_inf.transcribe_mic,
                                   inputs=params + whisper_params.as_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])
 
                 with gr.TabItem("T2T Translation"):  # tab 4
                     with gr.Row():