app.py

import os
import argparse
import gradio as gr
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)
from modules.utils.files_manager import load_yaml
from modules.whisper.whisper_factory import WhisperFactory
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 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.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 = 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.default_params = load_yaml(DEFAULT_PARAMETERS_CONFIG_PATH)
        print(f"Use \"{self.args.whisper_type}\" implementation")
        print(f"Device \"{self.whisper_inf.device}\" is detected")

    def create_whisper_parameters(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")
            dd_lang = gr.Dropdown(choices=["Automatic Detection"] + self.whisper_inf.available_langs,
                                  value=whisper_params["lang"], label="Language")
            dd_file_format = gr.Dropdown(choices=["SRT", "WebVTT", "txt"], value="SRT", 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):
            nb_beam_size = gr.Number(label="Beam Size", value=whisper_params["beam_size"], precision=0, interactive=True,
                                     info="Beam size to use for decoding.")
            nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=whisper_params["log_prob_threshold"], interactive=True,
                                              info="If the average log probability over sampled tokens is below this value, treat as failed.")
            nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=whisper_params["no_speech_threshold"], interactive=True,
                                               info="If the no speech probability is higher than this value AND the average log probability over sampled tokens is below 'Log Prob Threshold', consider the segment as silent.")
            dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types,
                                          value=self.whisper_inf.current_compute_type, interactive=True,
                                          info="Select the type of computation to perform.")
            nb_best_of = gr.Number(label="Best Of", value=whisper_params["best_of"], interactive=True,
                                   info="Number of candidates when sampling with non-zero temperature.")
            nb_patience = gr.Number(label="Patience", value=whisper_params["patience"], interactive=True,
                                    info="Beam search patience factor.")
            cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=whisper_params["condition_on_previous_text"],
                                                        interactive=True,
                                                        info="Condition on previous text during decoding.")
            sld_prompt_reset_on_temperature = gr.Slider(label="Prompt Reset On Temperature", value=whisper_params["prompt_reset_on_temperature"],
                                                        minimum=0, maximum=1, step=0.01, interactive=True,
                                                        info="Resets prompt if temperature is above this value."
                                                             " Arg has effect only if 'Condition On Previous Text' is True.")
            tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True,
                                           info="Initial prompt to use for decoding.")
            sd_temperature = gr.Slider(label="Temperature", value=whisper_params["temperature"], minimum=0.0,
                                       step=0.01, maximum=1.0, interactive=True,
                                       info="Temperature for sampling. It can be a tuple of temperatures, which will be successively used upon failures according to either `Compression Ratio Threshold` or `Log Prob Threshold`.")
            nb_compression_ratio_threshold = gr.Number(label="Compression Ratio Threshold", value=whisper_params["compression_ratio_threshold"],
                                                       interactive=True,
                                                       info="If the gzip compression ratio is above this value, treat as failed.")
            with gr.Group(visible=isinstance(self.whisper_inf, FasterWhisperInference)):
                nb_length_penalty = gr.Number(label="Length Penalty", value=whisper_params["length_penalty"],
                                              info="Exponential length penalty constant.")
                nb_repetition_penalty = gr.Number(label="Repetition Penalty", value=whisper_params["repetition_penalty"],
                                                  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=whisper_params["no_repeat_ngram_size"],
                                                    precision=0,
                                                    info="Prevent repetitions of n-grams with this size (set 0 to disable).")
                tb_prefix = gr.Textbox(label="Prefix", value=lambda: whisper_params["prefix"],
                                       info="Optional text to provide as a prefix for the first window.")
                cb_suppress_blank = gr.Checkbox(label="Suppress Blank", value=whisper_params["suppress_blank"],
                                                info="Suppress blank outputs at the beginning of the sampling.")
                tb_suppress_tokens = gr.Textbox(label="Suppress Tokens", value=whisper_params["suppress_tokens"],
                                                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=whisper_params["max_initial_timestamp"],
                                                     info="The initial timestamp cannot be later than this.")
                cb_word_timestamps = gr.Checkbox(label="Word Timestamps", value=whisper_params["word_timestamps"],
                                                 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=whisper_params["prepend_punctuations"],
                                                     info="If 'Word Timestamps' is True, merge these punctuation symbols with the next word.")
                tb_append_punctuations = gr.Textbox(label="Append Punctuations", value=whisper_params["append_punctuations"],
                                                    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: whisper_params["max_new_tokens"],
                                              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: whisper_params["chunk_length"],
                                            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: whisper_params["hallucination_silence_threshold"],
                                                               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=lambda: whisper_params["hotwords"],
                                         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=lambda: whisper_params["language_detection_threshold"],
                                                            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=lambda: whisper_params["language_detection_segments"],
                                                           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=whisper_params["chunk_length_s"],
                                              precision=0)
                nb_batch_size = gr.Number(label="Batch Size", value=whisper_params["batch_size"], precision=0)

        with gr.Accordion("BGM Separation", open=False):
            cb_bgm_separation = gr.Checkbox(label="Enable BGM Separation Filter", value=uvr_params["is_separate_bgm"],
                                            interactive=True)
            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["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=uvr_params["save_file"])

        with gr.Accordion("VAD", open=False):
            cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=vad_params["vad_filter"],
                                        interactive=True)
            sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=vad_params["threshold"],
                                     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=vad_params["min_speech_duration_ms"],
                                                  info="Final speech chunks shorter than this time are thrown out")
            nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=vad_params["max_speech_duration_s"],
                                                 info="Maximum duration of speech chunks in \"seconds\". Chunks longer"
                                                        " than this time will be split at the timestamp of the last silence that"
                                                        " lasts more than 100ms (if any), to prevent aggressive cutting.")
            nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=vad_params["min_silence_duration_ms"],
                                                   info="In the end of each speech chunk wait for this time"
                                                        " before separating it")
            nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=vad_params["speech_pad_ms"],
                                         info="Final speech chunks are padded by this time each side")

        with gr.Accordion("Diarization", open=False):
            cb_diarize = gr.Checkbox(label="Enable Diarization", value=diarization_params["is_diarize"])
            tb_hf_token = gr.Text(label="HuggingFace Token", value=diarization_params["hf_token"],
                                  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,
                prompt_reset_on_temperature=sld_prompt_reset_on_temperature, is_bgm_separate=cb_bgm_separation,
                uvr_device=dd_uvr_device, uvr_model_size=dd_uvr_model_size, uvr_segment_size=nb_uvr_segment_size,
                uvr_save_file=cb_uvr_save_file
            ),
            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:
            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")
                        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="")

                    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():
                        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, dd_file_format, cb_timestamp]
                    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)

                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)

                    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():
                        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 + 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)

                with gr.TabItem("Mic"):  # tab3
                    with gr.Row():
                        mic_input = gr.Microphone(label="Record with Mic", type="filepath", interactive=True)

                    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():
                        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 + whisper_params.as_list(),
                                  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",
                                             file_types=['.vtt', '.srt'])

                    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=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["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
        )

    @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)
            print(f"The directory path {folder_path} has newly created.")

    @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)


# 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=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('--inbrowser', type=str2bool, default=True, nargs='?', const=True, help='Whether to automatically start Gradio app or not')
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()