Spaces:
Running
Running
| import os | |
| import argparse | |
| from modules.whisper.whisper_Inference import WhisperInference | |
| from modules.whisper.faster_whisper_inference import FasterWhisperInference | |
| from modules.whisper.insanely_fast_whisper_inference import InsanelyFastWhisperInference | |
| from modules.translation.nllb_inference import NLLBInference | |
| from ui.htmls import * | |
| from modules.utils.youtube_manager import get_ytmetas | |
| from modules.translation.deepl_api import DeepLAPI | |
| from modules.whisper.whisper_parameter import * | |
| class App: | |
| def __init__(self, args): | |
| 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( | |
| model_dir=self.args.nllb_model_dir, | |
| output_dir=self.args.output_dir | |
| ) | |
| self.deepl_api = DeepLAPI( | |
| output_dir=self.args.output_dir | |
| ) | |
| def init_whisper(self): | |
| # Temporal fix of the issue : https://github.com/jhj0517/Whisper-WebUI/issues/144 | |
| os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' | |
| whisper_type = self.args.whisper_type.lower().strip() | |
| if whisper_type in ["faster_whisper", "faster-whisper", "fasterwhisper"]: | |
| whisper_inf = FasterWhisperInference( | |
| model_dir=self.args.faster_whisper_model_dir, | |
| output_dir=self.args.output_dir, | |
| args=self.args | |
| ) | |
| elif whisper_type in ["whisper"]: | |
| whisper_inf = WhisperInference( | |
| model_dir=self.args.whisper_model_dir, | |
| output_dir=self.args.output_dir, | |
| args=self.args | |
| ) | |
| elif whisper_type in ["insanely_fast_whisper", "insanely-fast-whisper", "insanelyfastwhisper", | |
| "insanely_faster_whisper", "insanely-faster-whisper", "insanelyfasterwhisper"]: | |
| whisper_inf = InsanelyFastWhisperInference( | |
| model_dir=self.args.insanely_fast_whisper_model_dir, | |
| output_dir=self.args.output_dir, | |
| args=self.args | |
| ) | |
| else: | |
| whisper_inf = FasterWhisperInference( | |
| model_dir=self.args.faster_whisper_model_dir, | |
| output_dir=self.args.output_dir, | |
| args=self.args | |
| ) | |
| return whisper_inf | |
| def open_folder(folder_path: str): | |
| if os.path.exists(folder_path): | |
| os.system(f"start {folder_path}") | |
| else: | |
| print(f"The folder {folder_path} does not exist.") | |
| 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) | |
| def launch(self): | |
| with self.app: | |
| with gr.Row(): | |
| 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("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.Accordion("VAD", 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, 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_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("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()) | |
| with gr.Accordion("Insanely Fast Whisper Parameters", open=False, 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.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 = [input_file, 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, | |
| window_size_sample=nb_window_size_sample, | |
| 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) | |
| 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(): | |
| 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("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.Accordion("VAD", 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, 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_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("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()) | |
| with gr.Accordion("Insanely Fast Whisper Parameters", open=False, | |
| 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.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) | |
| 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, | |
| window_size_sample=nb_window_size_sample, | |
| 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) | |
| btn_run.click(fn=self.whisper_inf.transcribe_youtube, | |
| inputs=params + whisper_params.as_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]) | |
| 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.Accordion("VAD", 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, 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_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("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()) | |
| with gr.Accordion("Insanely Fast Whisper Parameters", open=False, | |
| 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.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) | |
| 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, | |
| window_size_sample=nb_window_size_sample, | |
| 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) | |
| 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(): | |
| file_subs = gr.Files(type="filepath", label="Upload Subtitle Files to translate here", | |
| file_types=['.vtt', '.srt']) | |
| 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.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"): # 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.nllb_inf.translate_file, | |
| inputs=[file_subs, dd_nllb_model, dd_nllb_sourcelang, dd_nllb_targetlang, cb_timestamp], | |
| outputs=[tb_indicator, files_subtitles]) | |
| btn_openfolder.click(fn=lambda: self.open_folder(os.path.join("outputs", "translations")), | |
| inputs=None, | |
| outputs=None) | |
| # 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) | |
| if self.args.root_path: | |
| launch_args['root_path'] = self.args.root_path | |
| launch_args['inbrowser'] = True | |
| self.app.queue(api_open=False).launch(**launch_args) | |
| # Create the parser for command-line arguments | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--whisper_type', type=str, default="faster-whisper", help='A type of the whisper implementation between: ["whisper", "faster-whisper", "insanely-fast-whisper"]') | |
| parser.add_argument('--share', type=bool, default=False, nargs='?', const=True, help='Gradio share value') | |
| parser.add_argument('--server_name', type=str, default=None, help='Gradio server host') | |
| parser.add_argument('--server_port', type=int, default=None, help='Gradio server port') | |
| parser.add_argument('--root_path', type=str, default=None, help='Gradio root path') | |
| parser.add_argument('--username', type=str, default=None, help='Gradio authentication username') | |
| 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('--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') | |
| parser.add_argument('--insanely_fast_whisper_model_dir', type=str, default=os.path.join("models", "Whisper", "insanely-fast-whisper"), help='Directory path of the insanely-fast-whisper model') | |
| parser.add_argument('--diarization_model_dir', type=str, default=os.path.join("models", "Diarization"), help='Directory path of the diarization model') | |
| parser.add_argument('--nllb_model_dir', type=str, default=os.path.join("models", "NLLB"), help='Directory path of the Facebook NLLB model') | |
| parser.add_argument('--output_dir', type=str, default=os.path.join("outputs"), help='Directory path of the outputs') | |
| _args = parser.parse_args() | |
| if __name__ == "__main__": | |
| app = App(args=_args) | |
| app.launch() | |