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import gradio as gr
import os
import argparse
from modules.whisper_Inference import WhisperInference
from modules.faster_whisper_inference import FasterWhisperInference
from modules.nllb_inference import NLLBInference
from ui.htmls import *
from modules.youtube_manager import get_ytmetas
from modules.deepl_api import DeepLAPI
from modules.whisper_parameter import *
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()
if isinstance(self.whisper_inf, FasterWhisperInference):
print("Use Faster Whisper implementation")
else:
print("Use Open AI Whisper implementation")
print(f"Device \"{self.whisper_inf.device}\" is detected")
self.nllb_inf = NLLBInference()
self.deepl_api = DeepLAPI()
@staticmethod
def open_folder(folder_path: str):
if os.path.exists(folder_path):
os.system(f"start {folder_path}")
else:
print(f"The folder {folder_path} does not exist.")
@staticmethod
def on_change_models(model_size: str):
translatable_model = ["large", "large-v1", "large-v2", "large-v3"]
if model_size not in translatable_model:
return gr.Checkbox(visible=False, value=False, interactive=False)
else:
return gr.Checkbox(visible=True, value=False, label="Translate to English?", interactive=True)
def launch(self):
with self.app:
with gr.Row():
with gr.Column():
gr.Markdown(MARKDOWN, elem_id="md_project")
with gr.Tabs():
with gr.TabItem("File"): # tab1
with gr.Row():
input_file = gr.Files(type="filepath", label="Upload File here")
with gr.Row():
dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-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):
cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True)
nb_best_of = gr.Number(label="Best Of", value=5, interactive=True)
nb_patience = gr.Number(label="Patience", value=1, interactive=True)
cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True)
tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True)
sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True)
nb_compression_ratio_threshold = gr.Number(label="Compression Ratio Threshold", value=2.4, interactive=True)
with gr.Row():
btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
with gr.Row():
tb_indicator = gr.Textbox(label="Output", scale=5)
files_subtitles = gr.Files(label="Downloadable output file", scale=3, interactive=False)
btn_openfolder = gr.Button('📂', scale=1)
params = [input_file, dd_file_format, cb_timestamp]
whisper_params = WhisperGradioComponents(model_size=dd_model,
lang=dd_lang,
is_translate=cb_translate,
beam_size=nb_beam_size,
log_prob_threshold=nb_log_prob_threshold,
no_speech_threshold=nb_no_speech_threshold,
compute_type=dd_compute_type,
best_of=nb_best_of,
patience=nb_patience,
condition_on_previous_text=cb_condition_on_previous_text,
initial_prompt=tb_initial_prompt,
temperature=sd_temperature,
compression_ratio_threshold=nb_compression_ratio_threshold,
vad_filter=cb_vad_filter)
btn_run.click(fn=self.whisper_inf.transcribe_file,
inputs=params + whisper_params.to_list(),
outputs=[tb_indicator, files_subtitles])
btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])
with gr.TabItem("Youtube"): # tab2
with gr.Row():
tb_youtubelink = gr.Textbox(label="Youtube Link")
with gr.Row(equal_height=True):
with gr.Column():
img_thumbnail = gr.Image(label="Youtube Thumbnail")
with gr.Column():
tb_title = gr.Label(label="Youtube Title")
tb_description = gr.Textbox(label="Youtube Description", max_lines=15)
with gr.Row():
dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-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):
cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True)
nb_best_of = gr.Number(label="Best Of", value=5, interactive=True)
nb_patience = gr.Number(label="Patience", value=1, interactive=True)
cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True)
tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True)
sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True)
nb_compression_ratio_threshold = gr.Number(label="Compression Ratio Threshold", value=2.4, interactive=True)
with gr.Row():
btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
with gr.Row():
tb_indicator = gr.Textbox(label="Output", scale=5)
files_subtitles = gr.Files(label="Downloadable output file", scale=3)
btn_openfolder = gr.Button('📂', scale=1)
params = [tb_youtubelink, dd_file_format, cb_timestamp]
whisper_params = WhisperGradioComponents(model_size=dd_model,
lang=dd_lang,
is_translate=cb_translate,
beam_size=nb_beam_size,
log_prob_threshold=nb_log_prob_threshold,
no_speech_threshold=nb_no_speech_threshold,
compute_type=dd_compute_type,
best_of=nb_best_of,
patience=nb_patience,
condition_on_previous_text=cb_condition_on_previous_text,
initial_prompt=tb_initial_prompt,
temperature=sd_temperature,
compression_ratio_threshold=nb_compression_ratio_threshold,
vad_filter=cb_vad_filter)
btn_run.click(fn=self.whisper_inf.transcribe_youtube,
inputs=params + whisper_params.to_list(),
outputs=[tb_indicator, files_subtitles])
tb_youtubelink.change(get_ytmetas, inputs=[tb_youtubelink],
outputs=[img_thumbnail, tb_title, tb_description])
btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])
with gr.TabItem("Mic"): # tab3
with gr.Row():
mic_input = gr.Microphone(label="Record with Mic", type="filepath", interactive=True)
with gr.Row():
dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-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):
cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True)
nb_best_of = gr.Number(label="Best Of", value=5, interactive=True)
nb_patience = gr.Number(label="Patience", value=1, interactive=True)
cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True)
tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True)
sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True)
with gr.Row():
btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary")
with gr.Row():
tb_indicator = gr.Textbox(label="Output", scale=5)
files_subtitles = gr.Files(label="Downloadable output file", scale=3)
btn_openfolder = gr.Button('📂', scale=1)
params = [mic_input, dd_file_format]
whisper_params = WhisperGradioComponents(model_size=dd_model,
lang=dd_lang,
is_translate=cb_translate,
beam_size=nb_beam_size,
log_prob_threshold=nb_log_prob_threshold,
no_speech_threshold=nb_no_speech_threshold,
compute_type=dd_compute_type,
best_of=nb_best_of,
patience=nb_patience,
condition_on_previous_text=cb_condition_on_previous_text,
initial_prompt=tb_initial_prompt,
temperature=sd_temperature,
compression_ratio_threshold=nb_compression_ratio_threshold,
vad_filter=cb_vad_filter)
btn_run.click(fn=self.whisper_inf.transcribe_mic,
inputs=params + whisper_params.to_list(),
outputs=[tb_indicator, files_subtitles])
btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None)
dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate])
with gr.TabItem("T2T Translation"): # tab 4
with gr.Row():
file_subs = gr.Files(type="filepath", label="Upload Subtitle Files to translate here",
file_types=['.vtt', '.srt'])
with gr.TabItem("DeepL API"): # sub tab1
with gr.Row():
tb_authkey = gr.Textbox(label="Your Auth Key (API KEY)",
value="")
with gr.Row():
dd_deepl_sourcelang = gr.Dropdown(label="Source Language", value="Automatic Detection",
choices=list(
self.deepl_api.available_source_langs.keys()))
dd_deepl_targetlang = gr.Dropdown(label="Target Language", value="English",
choices=list(
self.deepl_api.available_target_langs.keys()))
with gr.Row():
cb_deepl_ispro = gr.Checkbox(label="Pro User?", value=False)
with gr.Row():
btn_run = gr.Button("TRANSLATE SUBTITLE FILE", variant="primary")
with gr.Row():
tb_indicator = gr.Textbox(label="Output", scale=5)
files_subtitles = gr.Files(label="Downloadable output file", scale=3)
btn_openfolder = gr.Button('📂', scale=1)
btn_run.click(fn=self.deepl_api.translate_deepl,
inputs=[tb_authkey, file_subs, dd_deepl_sourcelang, dd_deepl_targetlang,
cb_deepl_ispro],
outputs=[tb_indicator, files_subtitles])
btn_openfolder.click(fn=lambda: self.open_folder(os.path.join("outputs", "translations")),
inputs=None,
outputs=None)
with gr.TabItem("NLLB"): # sub tab2
with gr.Row():
dd_nllb_model = gr.Dropdown(label="Model", value=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=5)
files_subtitles = gr.Files(label="Downloadable output file", scale=3)
btn_openfolder = gr.Button('📂', scale=1)
with gr.Column():
md_vram_table = gr.HTML(NLLB_VRAM_TABLE, elem_id="md_nllb_vram_table")
btn_run.click(fn=self.nllb_inf.translate_file,
inputs=[file_subs, dd_nllb_model, dd_nllb_sourcelang, dd_nllb_targetlang, cb_timestamp],
outputs=[tb_indicator, files_subtitles])
btn_openfolder.click(fn=lambda: self.open_folder(os.path.join("outputs", "translations")),
inputs=None,
outputs=None)
# Launch the app with optional gradio settings
launch_args = {}
if self.args.share:
launch_args['share'] = self.args.share
if self.args.server_name:
launch_args['server_name'] = self.args.server_name
if self.args.server_port:
launch_args['server_port'] = self.args.server_port
if self.args.username and self.args.password:
launch_args['auth'] = (self.args.username, self.args.password)
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')
parser.add_argument('--api_open', type=bool, default=False, nargs='?', const=True, help='enable api or not')
_args = parser.parse_args()
if __name__ == "__main__":
app = App(args=_args)
app.launch()
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