import gradio as gr import os import argparse from modules.whisper_Inference import WhisperInference from modules.faster_whisper_inference import FasterWhisperInference from modules.nllb_inference import NLLBInference from ui.htmls import * from modules.youtube_manager import get_ytmetas from modules.deepl_api import DeepLAPI from modules.whisper_parameter import * # Ensure the outputs directory exists def ensure_output_directory(): output_directories = ["outputs", "outputs/translations"] for directory in output_directories: if not os.path.exists(directory): os.makedirs(directory) class App: def __init__(self, args): ensure_output_directory() # Making sure the output directories exist 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() 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 elif whisper_type == "whisper": whisper_inf = WhisperInference() whisper_inf.model_dir = self.args.whisper_model_dir else: # Default to FasterWhisperInference whisper_inf = FasterWhisperInference() whisper_inf.model_dir = self.args.faster_whisper_model_dir return whisper_inf @staticmethod 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.") @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) 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("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) 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) btn_run.click(fn=self.whisper_inf.transcribe_file, 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]) 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) 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]) 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) 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]) 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) 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"]') 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('--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') _args = parser.parse_args() if __name__ == "__main__": app = App(args=_args) app.launch()