repo_description: A Web UI for easy subtitle using whisper model.
repo_topics: ai gradio open-source python pytorch web-ui whisper
code: import os
  import argparse
  import gradio as gr
  from gradio_i18n import Translate, gettext as _
  import yaml
  
  from modules.utils.paths import (FASTER_WHISPER_MODELS_DIR, DIARIZATION_MODELS_DIR, OUTPUT_DIR, WHISPER_MODELS_DIR,
                                   INSANELY_FAST_WHISPER_MODELS_DIR, NLLB_MODELS_DIR, DEFAULT_PARAMETERS_CONFIG_PATH,
                                   UVR_MODELS_DIR, I18N_YAML_PATH)
  from modules.utils.files_manager import load_yaml, MEDIA_EXTENSION
  from modules.whisper.whisper_factory import WhisperFactory
  from modules.translation.nllb_inference import NLLBInference
  from modules.ui.htmls import *
  from modules.utils.cli_manager import str2bool
  from modules.utils.youtube_manager import get_ytmetas
  from modules.translation.deepl_api import DeepLAPI
  from modules.whisper.data_classes import *
  from modules.utils.logger import get_logger
  
  
  logger = get_logger()
  
  
  class App:
      def __init__(self, args):
          self.args = args
          # Check every 1 hour (3600) for cached files and delete them if older than 1 day (86400)
          self.app = gr.Blocks(css=CSS, theme=self.args.theme, delete_cache=(3600, 86400))
          self.whisper_inf = WhisperFactory.create_whisper_inference(
              whisper_type=self.args.whisper_type,
              whisper_model_dir=self.args.whisper_model_dir,
              faster_whisper_model_dir=self.args.faster_whisper_model_dir,
              insanely_fast_whisper_model_dir=self.args.insanely_fast_whisper_model_dir,
              uvr_model_dir=self.args.uvr_model_dir,
              output_dir=self.args.output_dir,
          )
          self.nllb_inf = NLLBInference(
              model_dir=self.args.nllb_model_dir,
              output_dir=os.path.join(self.args.output_dir, "translations")
          )
          self.deepl_api = DeepLAPI(
              output_dir=os.path.join(self.args.output_dir, "translations")
          )
          self.i18n = load_yaml(I18N_YAML_PATH)
          self.default_params = load_yaml(DEFAULT_PARAMETERS_CONFIG_PATH)
          logger.info(f"Use \"{self.args.whisper_type}\" implementation\n"
                      f"Device \"{self.whisper_inf.device}\" is detected")
  
      def create_pipeline_inputs(self):
          whisper_params = self.default_params["whisper"]
          vad_params = self.default_params["vad"]
          diarization_params = self.default_params["diarization"]
          uvr_params = self.default_params["bgm_separation"]
  
          with gr.Row():
              dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value=whisper_params["model_size"],
                                     label=_("Model"), allow_custom_value=True)
              dd_lang = gr.Dropdown(choices=self.whisper_inf.available_langs + [AUTOMATIC_DETECTION],
                                    value=AUTOMATIC_DETECTION if whisper_params["lang"] == AUTOMATIC_DETECTION.unwrap()
                                    else whisper_params["lang"], label=_("Language"))
              dd_file_format = gr.Dropdown(choices=["SRT", "WebVTT", "txt", "LRC"], value=whisper_params["file_format"], label=_("File Format"))
          with gr.Row():
              cb_translate = gr.Checkbox(value=whisper_params["is_translate"], label=_("Translate to English?"),
                                         interactive=True)
          with gr.Row():
              cb_timestamp = gr.Checkbox(value=whisper_params["add_timestamp"],
                                         label=_("Add a timestamp to the end of the filename"),
                                         interactive=True)
  
          with gr.Accordion(_("Advanced Parameters"), open=False):
              whisper_inputs = WhisperParams.to_gradio_inputs(defaults=whisper_params, only_advanced=True,
                                                              whisper_type=self.args.whisper_type,
                                                              available_compute_types=self.whisper_inf.available_compute_types,
                                                              compute_type=self.whisper_inf.current_compute_type)
  
          with gr.Accordion(_("Background Music Remover Filter"), open=False):
              uvr_inputs = BGMSeparationParams.to_gradio_input(defaults=uvr_params,
                                                               available_models=self.whisper_inf.music_separator.available_models,
                                                               available_devices=self.whisper_inf.music_separator.available_devices,
                                                               device=self.whisper_inf.music_separator.device)
  
          with gr.Accordion(_("Voice Detection Filter"), open=False):
              vad_inputs = VadParams.to_gradio_inputs(defaults=vad_params)
  
          with gr.Accordion(_("Diarization"), open=False):
              diarization_inputs = DiarizationParams.to_gradio_inputs(defaults=diarization_params,
                                                                      available_devices=self.whisper_inf.diarizer.available_device,
                                                                      device=self.whisper_inf.diarizer.device)
  
          pipeline_inputs = [dd_model, dd_lang, cb_translate] + whisper_inputs + vad_inputs + diarization_inputs + uvr_inputs
  
          return (
              pipeline_inputs,
              dd_file_format,
              cb_timestamp
          )
  
      def launch(self):
          translation_params = self.default_params["translation"]
          deepl_params = translation_params["deepl"]
          nllb_params = translation_params["nllb"]
          uvr_params = self.default_params["bgm_separation"]
  
          with self.app:
              lang = gr.Radio(choices=list(self.i18n.keys()),
                              label=_("Language"), interactive=True,
                              visible=False,  # Set it by development purpose.
                              )
              with Translate(self.i18n):  # Add `lang = lang` here to test dynamic change of the languages.
                  with gr.Row():
                      with gr.Column():
                          gr.Markdown(MARKDOWN, elem_id="md_project")
                  with gr.Tabs():
                      with gr.TabItem(_("File")):  # tab1
                          with gr.Column():
                              input_file = gr.Files(type="filepath", label=_("Upload File here"), file_types=MEDIA_EXTENSION)
                              tb_input_folder = gr.Textbox(label="Input Folder Path (Optional)",
                                                           info="Optional: Specify the folder path where the input files are located, if you prefer to use local files instead of uploading them."
                                                                " Leave this field empty if you do not wish to use a local path.",
                                                           visible=self.args.colab,
                                                           value="")
                              cb_include_subdirectory = gr.Checkbox(label="Include Subdirectory Files",
                                                                    info="When using Input Folder Path above, whether to include all files in the subdirectory or not.",
                                                                    visible=self.args.colab,
                                                                    value=False)
                              cb_save_same_dir = gr.Checkbox(label="Save outputs at same directory",
                                                             info="When using Input Folder Path above, whether to save output in the same directory as inputs or not, in addition to the original"
                                                                  " output directory.",
                                                             visible=self.args.colab,
                                                             value=True)
                          pipeline_params, dd_file_format, cb_timestamp = self.create_pipeline_inputs()
  
                          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, tb_input_folder, cb_include_subdirectory, cb_save_same_dir,
                                    dd_file_format, cb_timestamp]
                          params = params + pipeline_params
                          btn_run.click(fn=self.whisper_inf.transcribe_file,
                                        inputs=params,
                                        outputs=[tb_indicator, files_subtitles])
                          btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
  
                      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)
  
                          pipeline_params, dd_file_format, cb_timestamp = self.create_pipeline_inputs()
  
                          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]
  
                          btn_run.click(fn=self.whisper_inf.transcribe_youtube,
                                        inputs=params + pipeline_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)
  
                      with gr.TabItem(_("Mic")):  # tab3
                          with gr.Row():
                              mic_input = gr.Microphone(label=_("Record with Mic"), type="filepath", interactive=True,
                                                        show_download_button=True)
  
                          pipeline_params, dd_file_format, cb_timestamp = self.create_pipeline_inputs()
  
                          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, cb_timestamp]
  
                          btn_run.click(fn=self.whisper_inf.transcribe_mic,
                                        inputs=params + pipeline_params,
                                        outputs=[tb_indicator, files_subtitles])
                          btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
  
                      with gr.TabItem(_("T2T Translation")):  # tab 4
                          with gr.Row():
                              file_subs = gr.Files(type="filepath", label=_("Upload Subtitle Files to translate here"))
  
                          with gr.TabItem(_("DeepL API")):  # sub tab1
                              with gr.Row():
                                  tb_api_key = gr.Textbox(label=_("Your Auth Key (API KEY)"),
                                                          value=deepl_params["api_key"])
                              with gr.Row():
                                  dd_source_lang = gr.Dropdown(label=_("Source Language"),
                                                               value=AUTOMATIC_DETECTION if deepl_params["source_lang"] == AUTOMATIC_DETECTION.unwrap()
                                                               else deepl_params["source_lang"],
                                                               choices=list(self.deepl_api.available_source_langs.keys()))
                                  dd_target_lang = gr.Dropdown(label=_("Target Language"),
                                                               value=deepl_params["target_lang"],
                                                               choices=list(self.deepl_api.available_target_langs.keys()))
                              with gr.Row():
                                  cb_is_pro = gr.Checkbox(label=_("Pro User?"), value=deepl_params["is_pro"])
                              with gr.Row():
                                  cb_timestamp = gr.Checkbox(value=translation_params["add_timestamp"],
                                                             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)
  
                          btn_run.click(fn=self.deepl_api.translate_deepl,
                                        inputs=[tb_api_key, file_subs, dd_source_lang, dd_target_lang,
                                                cb_is_pro, cb_timestamp],
                                        outputs=[tb_indicator, files_subtitles])
  
                          btn_openfolder.click(
                              fn=lambda: self.open_folder(os.path.join(self.args.output_dir, "translations")),
                              inputs=None,
                              outputs=None)
  
                          with gr.TabItem(_("NLLB")):  # sub tab2
                              with gr.Row():
                                  dd_model_size = gr.Dropdown(label=_("Model"), value=nllb_params["model_size"],
                                                              choices=self.nllb_inf.available_models)
                                  dd_source_lang = gr.Dropdown(label=_("Source Language"),
                                                               value=nllb_params["source_lang"],
                                                               choices=self.nllb_inf.available_source_langs)
                                  dd_target_lang = gr.Dropdown(label=_("Target Language"),
                                                               value=nllb_params["target_lang"],
                                                               choices=self.nllb_inf.available_target_langs)
                              with gr.Row():
                                  nb_max_length = gr.Number(label="Max Length Per Line", value=nllb_params["max_length"],
                                                            precision=0)
                              with gr.Row():
                                  cb_timestamp = gr.Checkbox(value=translation_params["add_timestamp"],
                                                             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_model_size, dd_source_lang, dd_target_lang,
                                                nb_max_length, cb_timestamp],
                                        outputs=[tb_indicator, files_subtitles])
  
                          btn_openfolder.click(
                              fn=lambda: self.open_folder(os.path.join(self.args.output_dir, "translations")),
                              inputs=None,
                              outputs=None)
  
                      with gr.TabItem(_("BGM Separation")):
                          files_audio = gr.Files(type="filepath", label=_("Upload Audio Files to separate background music"))
                          dd_uvr_device = gr.Dropdown(label=_("Device"), value=self.whisper_inf.music_separator.device,
                                                      choices=self.whisper_inf.music_separator.available_devices)
                          dd_uvr_model_size = gr.Dropdown(label=_("Model"), value=uvr_params["uvr_model_size"],
                                                          choices=self.whisper_inf.music_separator.available_models)
                          nb_uvr_segment_size = gr.Number(label="Segment Size", value=uvr_params["segment_size"],
                                                          precision=0)
                          cb_uvr_save_file = gr.Checkbox(label=_("Save separated files to output"),
                                                         value=True, visible=False)
                          btn_run = gr.Button(_("SEPARATE BACKGROUND MUSIC"), variant="primary")
                          with gr.Column():
                              with gr.Row():
                                  ad_instrumental = gr.Audio(label=_("Instrumental"), scale=8)
                                  btn_open_instrumental_folder = gr.Button('📂', scale=1)
                              with gr.Row():
                                  ad_vocals = gr.Audio(label=_("Vocals"), scale=8)
                                  btn_open_vocals_folder = gr.Button('📂', scale=1)
  
                          btn_run.click(fn=self.whisper_inf.music_separator.separate_files,
                                        inputs=[files_audio, dd_uvr_model_size, dd_uvr_device, nb_uvr_segment_size,
                                                cb_uvr_save_file],
                                        outputs=[ad_instrumental, ad_vocals])
                          btn_open_instrumental_folder.click(inputs=None,
                                                             outputs=None,
                                                             fn=lambda: self.open_folder(os.path.join(
                                                                 self.args.output_dir, "UVR", "instrumental"
                                                             )))
                          btn_open_vocals_folder.click(inputs=None,
                                                       outputs=None,
                                                       fn=lambda: self.open_folder(os.path.join(
                                                           self.args.output_dir, "UVR", "vocals"
                                                       )))
  
          # Launch the app with optional gradio settings
          args = self.args
          self.app.queue(
              api_open=args.api_open
          ).launch(
              share=args.share,
              server_name=args.server_name,
              server_port=args.server_port,
              auth=(args.username, args.password) if args.username and args.password else None,
              root_path=args.root_path,
              inbrowser=args.inbrowser,
              ssl_verify=args.ssl_verify,
              ssl_keyfile=args.ssl_keyfile,
              ssl_keyfile_password=args.ssl_keyfile_password,
              ssl_certfile=args.ssl_certfile,
              allowed_paths=eval(args.allowed_paths) if args.allowed_paths else None
          )
  
      @staticmethod
      def open_folder(folder_path: str):
          if os.path.exists(folder_path):
              os.system(f"start {folder_path}")
          else:
              os.makedirs(folder_path, exist_ok=True)
              logger.info(f"The directory path {folder_path} has newly created.")
  
  
  parser = argparse.ArgumentParser()
  parser.add_argument('--whisper_type', type=str, default=WhisperImpl.FASTER_WHISPER.value,
                      choices=[item.value for item in WhisperImpl],
                      help='A type of the whisper implementation (Github repo name)')
  parser.add_argument('--share', type=str2bool, 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=str2bool, default=False, nargs='?', const=True, help='Is colab user or not')
  parser.add_argument('--api_open', type=str2bool, default=False, nargs='?', const=True,
                      help='Enable api or not in Gradio')
  parser.add_argument('--allowed_paths', type=str, default=None, help='Gradio allowed paths')
  parser.add_argument('--inbrowser', type=str2bool, default=True, nargs='?', const=True,
                      help='Whether to automatically start Gradio app or not')
  parser.add_argument('--ssl_verify', type=str2bool, default=True, nargs='?', const=True,
                      help='Whether to verify SSL or not')
  parser.add_argument('--ssl_keyfile', type=str, default=None, help='SSL Key file location')
  parser.add_argument('--ssl_keyfile_password', type=str, default=None, help='SSL Key file password')
  parser.add_argument('--ssl_certfile', type=str, default=None, help='SSL cert file location')
  parser.add_argument('--whisper_model_dir', type=str, default=WHISPER_MODELS_DIR,
                      help='Directory path of the whisper model')
  parser.add_argument('--faster_whisper_model_dir', type=str, default=FASTER_WHISPER_MODELS_DIR,
                      help='Directory path of the faster-whisper model')
  parser.add_argument('--insanely_fast_whisper_model_dir', type=str,
                      default=INSANELY_FAST_WHISPER_MODELS_DIR,
                      help='Directory path of the insanely-fast-whisper model')
  parser.add_argument('--diarization_model_dir', type=str, default=DIARIZATION_MODELS_DIR,
                      help='Directory path of the diarization model')
  parser.add_argument('--nllb_model_dir', type=str, default=NLLB_MODELS_DIR,
                      help='Directory path of the Facebook NLLB model')
  parser.add_argument('--uvr_model_dir', type=str, default=UVR_MODELS_DIR,
                      help='Directory path of the UVR model')
  parser.add_argument('--output_dir', type=str, default=OUTPUT_DIR, help='Directory path of the outputs')
  _args = parser.parse_args()
  
  if __name__ == "__main__":
      app = App(args=_args)
      app.launch()
  