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app.py
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app.py
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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, delete_cache=(60, 3600))
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,
allow_custom_value=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.")
nb_chunk_length = gr.Number(label="Chunk Length (s)", 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.")
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_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_batch_size = gr.Number(label="Batch Size", value=whisper_params["batch_size"], precision=0)
with gr.Accordion("Background Music Remover Filter", open=False):
cb_bgm_separation = gr.Checkbox(label="Enable Background Music Remover Filter", value=uvr_params["is_separate_bgm"],
interactive=True,
info="Enabling this will remove background music by submodel before"
" transcribing ")
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"])
cb_uvr_enable_offload = gr.Checkbox(label="Offload sub model after removing background music",
value=uvr_params["enable_offload"])
with gr.Accordion("Voice Detection Filter", open=False):
cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=vad_params["vad_filter"],
interactive=True,
info="Enable this to transcribe only detected voice parts by submodel.")
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\".")
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 "
"\"https://huggingface.co/pyannote/segmentation-3.0\" 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=nb_chunk_length, 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,
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, uvr_enable_offload=cb_uvr_enable_offload
),
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")
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()