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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 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", | |
| 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 | |
| ) | |
| 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.") | |
| 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() | |