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Running
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T4
| import spaces | |
| import torch | |
| 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 | |
| class App: | |
| def __init__(self, args): | |
| self.args = args | |
| self.app = gr.Blocks(css=CSS, theme=self.args.theme) | |
| #self.whisper_inf = WhisperInference() if self.args.disable_faster_whisper else FasterWhisperInference() | |
| #NOTE: Faster whisper is not able to use in HuggingFace space. see more info : https://huggingface.co/spaces/jhj0517/Whisper-WebUI/discussions/1 | |
| self.whisper_inf = WhisperInference() | |
| print("Use Open AI Whisper implementation") | |
| print(f"Device \"{self.whisper_inf.device}\" is detected") | |
| cuda_version = torch.version.cuda | |
| print(f"CUDA version: {cuda_version}") | |
| cudnn_version = torch.backends.cudnn.version() | |
| print(f"cuDNN version: {cudnn_version}") | |
| self.nllb_inf = NLLBInference() | |
| self.deepl_api = DeepLAPI() | |
| 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-v3", | |
| 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) | |
| with gr.Row(): | |
| btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary") | |
| with gr.Row(): | |
| tb_indicator = gr.Textbox(label="Output", scale=4) | |
| files_subtitles = gr.Files(label="Downloadable output file", scale=4, interactive=False) | |
| btn_openfolder = gr.Button('📂', scale=1) | |
| params = [input_file, dd_model, dd_lang, dd_file_format, cb_translate, cb_timestamp] | |
| advanced_params = [nb_beam_size, nb_log_prob_threshold, nb_no_speech_threshold, dd_compute_type] | |
| btn_run.click(fn=self.whisper_inf.transcribe_file, | |
| inputs=params + advanced_params, | |
| 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-v3", | |
| 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) | |
| with gr.Row(): | |
| btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary") | |
| with gr.Row(): | |
| tb_indicator = gr.Textbox(label="Output", scale=4) | |
| files_subtitles = gr.Files(label="Downloadable output file", scale=4) | |
| btn_openfolder = gr.Button('📂', scale=1) | |
| params = [tb_youtubelink, dd_model, dd_lang, dd_file_format, cb_translate, cb_timestamp] | |
| advanced_params = [nb_beam_size, nb_log_prob_threshold, nb_no_speech_threshold, dd_compute_type] | |
| btn_run.click(fn=self.whisper_inf.transcribe_youtube, | |
| inputs=params + advanced_params, | |
| 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-v3", | |
| 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) | |
| with gr.Row(): | |
| btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary") | |
| with gr.Row(): | |
| tb_indicator = gr.Textbox(label="Output", scale=4) | |
| files_subtitles = gr.Files(label="Downloadable output file", scale=4) | |
| btn_openfolder = gr.Button('📂', scale=1) | |
| params = [mic_input, dd_model, dd_lang, dd_file_format, cb_translate] | |
| advanced_params = [nb_beam_size, nb_log_prob_threshold, nb_no_speech_threshold, dd_compute_type] | |
| btn_run.click(fn=self.whisper_inf.transcribe_mic, | |
| inputs=params + advanced_params, | |
| 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=4) | |
| files_subtitles = gr.Files(label="Downloadable output file", scale=4) | |
| 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=self.nllb_inf.default_model_size, | |
| 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=4) | |
| files_subtitles = gr.Files(label="Downloadable output file", scale=4) | |
| 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) | |
| self.app.queue(api_open=False).launch(**launch_args) | |
| # Create the parser for command-line arguments | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--disable_faster_whisper', type=bool, default=False, nargs='?', const=True, help='Disable the faster_whisper implementation. faster_whipser is implemented by https://github.com/guillaumekln/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') | |
| _args = parser.parse_args() | |
| if __name__ == "__main__": | |
| app = App(args=_args) | |
| app.launch() | |