Upload app.py

app.py

@@ -0,0 +1,358 @@
+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 = self.init_whisper()
+        print(f"Use \"{self.args.whisper_type}\" implementation")
+        print(f"Device \"{self.whisper_inf.device}\" is detected")
+        self.nllb_inf = NLLBInference()
+        self.deepl_api = DeepLAPI()
+
+    def init_whisper(self):
+        whisper_type = self.args.whisper_type.lower().strip()
+
+        if whisper_type in ["faster_whisper", "faster-whisper"]:
+            whisper_inf = FasterWhisperInference()
+            whisper_inf.model_dir = self.args.faster_whisper_model_dir
+        if whisper_type in ["whisper"]:
+            whisper_inf = WhisperInference()
+            whisper_inf.model_dir = self.args.whisper_model_dir
+        else:
+            whisper_inf = FasterWhisperInference()
+            whisper_inf.model_dir = self.args.faster_whisper_model_dir
+        return whisper_inf
+
+    @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-v2",
+                                               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("VAD Options", open=False, visible=isinstance(self.whisper_inf, FasterWhisperInference)):
+                        cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
+                        sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5)
+                        nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250)
+                        nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999)
+                        nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000)
+                        nb_window_size_sample = gr.Number(label="Window Size (samples)", precision=0, value=1024)
+                        nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
+                    with gr.Accordion("Advanced_Parameters", open=False):
+                        nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
+                        nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
+                        nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
+                        dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True)
+                        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,
+                                                             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,
+                                                             window_size_sample=nb_window_size_sample,
+                                                             speech_pad_ms=nb_speech_pad_ms)
+
+                    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-v2",
+                                               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("VAD Options", open=False, visible=isinstance(self.whisper_inf, FasterWhisperInference)):
+                        cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
+                        sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5)
+                        nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250)
+                        nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999)
+                        nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000)
+                        nb_window_size_sample = gr.Number(label="Window Size (samples)", precision=0, value=1024)
+                        nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
+                    with gr.Accordion("Advanced_Parameters", open=False):
+                        nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
+                        nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
+                        nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
+                        dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True)
+                        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,
+                                                             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,
+                                                             window_size_sample=nb_window_size_sample,
+                                                             speech_pad_ms=nb_speech_pad_ms)
+                    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-v2",
+                                               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("VAD Options", open=False, visible=isinstance(self.whisper_inf, FasterWhisperInference)):
+                        cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True)
+                        sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5)
+                        nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250)
+                        nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999)
+                        nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000)
+                        nb_window_size_sample = gr.Number(label="Window Size (samples)", precision=0, value=1024)
+                        nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400)
+                    with gr.Accordion("Advanced_Parameters", open=False):
+                        nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True)
+                        nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True)
+                        nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True)
+                        dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True)
+                        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,
+                                                             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,
+                                                             window_size_sample=nb_window_size_sample,
+                                                             speech_pad_ms=nb_speech_pad_ms)
+                    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="facebook/nllb-200-1.3B",
+                                                        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)
+        launch_args['inbrowser'] = True
+
+        self.app.queue(api_open=False).launch(**launch_args)
+
+
+# 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"]')
+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')
+parser.add_argument('--whisper_model_dir', type=str, default=os.path.join("models", "Whisper"), help='Directory path of the whisper model')
+parser.add_argument('--faster_whisper_model_dir', type=str, default=os.path.join("models", "Whisper", "faster-whisper"), help='Directory path of the faster-whisper model')
+_args = parser.parse_args()
+
+if __name__ == "__main__":
+    app = App(args=_args)
+    app.launch()