| import gradio as gr |
| import os |
| import glob |
| import subprocess |
| from pathlib import Path |
| from datetime import datetime |
| import json |
| import sys |
| import time |
| import random |
| from helpers import update_model_dropdown, handle_file_upload, clear_old_output, save_uploaded_file, update_file_list, clean_model, get_model_categories |
| from download import download_callback |
| from model import get_model_config, MODEL_CONFIGS, get_all_model_configs_with_custom, add_custom_model, delete_custom_model, get_custom_models_list, SUPPORTED_MODEL_TYPES, load_custom_models, get_model_chunk_size |
| from processing import process_audio, auto_ensemble_process, ensemble_audio_fn, refresh_auto_output |
| from assets.i18n.i18n import I18nAuto |
| from config_manager import load_config, save_config, update_favorites, save_preset, delete_preset |
| from phase_fixer import SOURCE_MODELS, TARGET_MODELS |
| import logging |
| logging.basicConfig(filename='sesa_gui.log', level=logging.WARNING) |
|
|
| |
| BASE_DIR = os.path.dirname(os.path.abspath(__file__)) |
| CONFIG_DIR = os.path.join(BASE_DIR, "assets") |
| CONFIG_FILE = os.path.join(CONFIG_DIR, "config.json") |
| URL_FILE = os.path.join(CONFIG_DIR, "last_url.txt") |
|
|
| |
| user_config = load_config() |
| initial_settings = user_config["settings"] |
| initial_favorites = user_config["favorites"] |
| initial_presets = user_config["presets"] |
|
|
| |
| if "auto_category" not in initial_settings or initial_settings["auto_category"] not in MODEL_CONFIGS: |
| initial_settings["auto_category"] = "Vocal Models" |
|
|
| |
| if not os.path.exists(CONFIG_FILE): |
| default_config = { |
| "lang": {"override": False, "selected_lang": "auto"}, |
| "sharing": { |
| "method": "gradio", |
| "ngrok_token": "", |
| "port": random.randint(1000, 9000) |
| } |
| } |
| os.makedirs(CONFIG_DIR, exist_ok=True) |
| with open(CONFIG_FILE, "w", encoding="utf-8") as f: |
| json.dump(default_config, f, indent=2) |
| else: |
| try: |
| with open(CONFIG_FILE, "r", encoding="utf-8") as f: |
| config = json.load(f) |
| |
| if "lang" not in config: |
| config["lang"] = {"override": False, "selected_lang": "auto"} |
| |
| if "sharing" not in config: |
| config["sharing"] = { |
| "method": "gradio", |
| "ngrok_token": "", |
| "port": random.randint(1000, 9000) |
| } |
| |
| with open(CONFIG_FILE, "w", encoding="utf-8") as f: |
| json.dump(config, f, indent=2) |
| except json.JSONDecodeError: |
| print("Warning: config.json is corrupted. Creating a new one.") |
| default_config = { |
| "lang": {"override": False, "selected_lang": "auto"}, |
| "sharing": { |
| "method": "gradio", |
| "ngrok_token": "", |
| "port": random.randint(1000, 9000) |
| } |
| } |
| with open(CONFIG_FILE, "w", encoding="utf-8") as f: |
| json.dump(default_config, f, indent=2) |
|
|
| |
| i18n = I18nAuto() |
|
|
| |
| OUTPUT_FORMATS = ['wav', 'flac', 'mp3', 'ogg', 'opus', 'm4a', 'aiff', 'ac3'] |
|
|
| |
| def create_interface(): |
| css = """ |
| body { |
| background: linear-gradient(to bottom, rgba(45, 11, 11, 0.9), rgba(0, 0, 0, 0.8)), url('/content/logo.jpg') no-repeat center center fixed; |
| background-size: cover; |
| min-height: 100vh; |
| margin: 0; |
| padding: 1rem; |
| font-family: 'Poppins', sans-serif; |
| color: #C0C0C0; |
| overflow-x: hidden; |
| } |
| .header-text { |
| text-align: center; |
| padding: 100px 20px 20px; |
| color: #ff4040; |
| font-size: 3rem; |
| font-weight: 900; |
| text-shadow: 0 0 10px rgba(255, 64, 64, 0.5); |
| z-index: 1500; |
| animation: text-glow 2s infinite; |
| } |
| .header-subtitle { |
| text-align: center; |
| color: #C0C0C0; |
| font-size: 1.2rem; |
| font-weight: 300; |
| margin-top: -10px; |
| text-shadow: 0 0 5px rgba(255, 64, 64, 0.3); |
| } |
| .gr-tab { |
| background: rgba(128, 0, 0, 0.5) !important; |
| border-radius: 12px 12px 0 0 !important; |
| margin: 0 5px !important; |
| color: #C0C0C0 !important; |
| border: 1px solid #ff4040 !important; |
| z-index: 1500; |
| transition: background 0.3s ease, color 0.3s ease; |
| padding: 10px 20px !important; |
| font-size: 1.1rem !important; |
| } |
| button { |
| transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important; |
| background: #800000 !important; |
| border: 1px solid #ff4040 !important; |
| color: #C0C0C0 !important; |
| border-radius: 8px !important; |
| padding: 8px 16px !important; |
| box-shadow: 0 2px 10px rgba(255, 64, 64, 0.3); |
| } |
| button:hover { |
| transform: scale(1.05) !important; |
| box-shadow: 0 10px 40px rgba(255, 64, 64, 0.7) !important; |
| background: #ff4040 !important; |
| } |
| .compact-upload.horizontal { |
| display: inline-flex !important; |
| align-items: center !important; |
| gap: 8px !important; |
| max-width: 400px !important; |
| height: 40px !important; |
| padding: 0 12px !important; |
| border: 1px solid #ff4040 !important; |
| background: rgba(128, 0, 0, 0.5) !important; |
| border-radius: 8px !important; |
| } |
| .compact-dropdown { |
| --padding: 8px 12px !important; |
| --radius: 10px !important; |
| border: 1px solid #ff4040 !important; |
| background: rgba(128, 0, 0, 0.5) !important; |
| color: #C0C0C0 !important; |
| } |
| #custom-progress { |
| margin-top: 10px; |
| padding: 10px; |
| background: rgba(128, 0, 0, 0.3); |
| border-radius: 8px; |
| border: 1px solid #ff4040; |
| } |
| #progress-bar { |
| height: 20px; |
| background: linear-gradient(90deg, #6e8efb, #a855f7, #ff4040); |
| background-size: 200% 100%; |
| border-radius: 5px; |
| transition: width 0.4s cubic-bezier(0.4, 0, 0.2, 1); |
| max-width: 100% !important; |
| } |
| @keyframes progress-shimmer { |
| 0% { background-position: 200% 0; } |
| 100% { background-position: -200% 0; } |
| } |
| #progress-bar[data-active="true"] { |
| animation: progress-shimmer 2s linear infinite; |
| } |
| .gr-accordion { |
| background: rgba(128, 0, 0, 0.5) !important; |
| border-radius: 10px !important; |
| border: 1px solid #ff4040 !important; |
| } |
| .footer { |
| text-align: center; |
| padding: 20px; |
| color: #ff4040; |
| font-size: 14px; |
| margin-top: 40px; |
| background: rgba(128, 0, 0, 0.3); |
| border-top: 1px solid #ff4040; |
| } |
| #log-accordion { |
| max-height: 400px; |
| overflow-y: auto; |
| background: rgba(0, 0, 0, 0.7) !important; |
| padding: 10px; |
| border-radius: 8px; |
| } |
| @keyframes text-glow { |
| 0% { text-shadow: 0 0 5px rgba(192, 192, 192, 0); } |
| 50% { text-shadow: 0 0 15px rgba(192, 192, 192, 1); } |
| 100% { text-shadow: 0 0 5px rgba(192, 192, 192, 0); } |
| } |
| """ |
|
|
| |
| user_config = load_config() |
| initial_settings = user_config["settings"] |
| initial_favorites = user_config["favorites"] |
| initial_presets = user_config["presets"] |
|
|
| with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo: |
| current_lang = gr.State(value=i18n.language) |
| favorites_state = gr.State(value=initial_favorites) |
| presets_state = gr.State(value=initial_presets) |
|
|
| header_html = gr.HTML( |
| value=f""" |
| <div class="header-text">{i18n("SESA Audio Separation")}</div> |
| <div class="header-subtitle">{i18n("ultimate_audio_separation")}</div> |
| """ |
| ) |
|
|
| with gr.Tabs(): |
| with gr.Tab(i18n("audio_separation_tab"), id="separation_tab"): |
| with gr.Row(equal_height=True): |
| with gr.Column(scale=1, min_width=380): |
| with gr.Accordion(i18n("input_model"), open=True) as input_model_accordion: |
| with gr.Tabs(): |
| with gr.Tab(i18n("upload")) as upload_tab: |
| input_audio_file = gr.File( |
| file_types=[".wav", ".mp3", ".m4a", ".mp4", ".mkv", ".flac"], |
| elem_classes=["compact-upload", "horizontal", "x-narrow"], |
| label="" |
| ) |
| with gr.Tab(i18n("path")) as path_tab: |
| file_path_input = gr.Textbox(placeholder=i18n("path_placeholder")) |
|
|
| with gr.Row(): |
| model_category = gr.Dropdown( |
| label=i18n("category"), |
| choices=[i18n(cat) for cat in get_all_model_configs_with_custom().keys()], |
| value=i18n(initial_settings["model_category"]) |
| ) |
| favorite_button = gr.Button(i18n("add_favorite"), variant="secondary", scale=0) |
|
|
| model_dropdown = gr.Dropdown( |
| label=i18n("model"), |
| choices=update_model_dropdown(i18n(initial_settings["model_category"]), favorites=initial_favorites)["choices"], |
| value=initial_settings["selected_model"] |
| ) |
|
|
| with gr.Accordion(i18n("settings"), open=False) as settings_accordion: |
| with gr.Row(): |
| with gr.Column(scale=1): |
| export_format = gr.Dropdown( |
| label=i18n("format"), |
| choices=['wav FLOAT', 'flac PCM_16', 'flac PCM_24'], |
| value=initial_settings["export_format"] |
| ) |
| with gr.Column(scale=1): |
| _init_cs_mode = initial_settings.get("chunk_size_mode", "base") |
| chunk_size_mode = gr.Radio( |
| label=i18n("chunk_size_mode"), |
| choices=["base", "custom", "yaml"], |
| value=_init_cs_mode, |
| info=i18n("chunk_size_mode_info") |
| ) |
| chunk_size = gr.Dropdown( |
| label=i18n("chunk_size"), |
| choices=[352800, 485100], |
| value=initial_settings["chunk_size"], |
| info=i18n("chunk_size_info"), |
| visible=(_init_cs_mode == "base") |
| ) |
| chunk_size_custom = gr.Number( |
| label=i18n("chunk_size_custom_label"), |
| value=initial_settings.get("chunk_size_custom", 352800), |
| precision=0, |
| info=i18n("chunk_size_custom_info"), |
| visible=(_init_cs_mode == "custom") |
| ) |
| chunk_size_yaml_display = gr.Textbox( |
| label=i18n("chunk_size_yaml_label"), |
| value=i18n("chunk_size_yaml_not_downloaded"), |
| interactive=False, |
| info=i18n("chunk_size_yaml_display_info"), |
| visible=(_init_cs_mode == "yaml") |
| ) |
|
|
| with gr.Row(): |
| with gr.Column(scale=2): |
| overlap = gr.Slider( |
| minimum=2, |
| maximum=50, |
| step=1, |
| label=i18n("overlap"), |
| value=initial_settings["overlap"], |
| info=i18n("overlap_info") |
| ) |
|
|
| with gr.Accordion(i18n("backend_settings"), open=True) as backend_settings_accordion: |
| gr.Markdown(f"### {i18n('inference_backend')} - {i18n('ultra_optimized_pytorch')}") |
| gr.Markdown(f"**{i18n('default_active_max_speed')}**") |
| |
| with gr.Row(): |
| optimize_mode = gr.Dropdown( |
| label=i18n("optimization_mode"), |
| choices=['channels_last', 'compile', 'default'], |
| value=initial_settings.get("optimize_mode", "channels_last"), |
| info=f"channels_last: {i18n('channels_last_mode')} | compile: {i18n('compile_mode')} | default: {i18n('default_mode')}" |
| ) |
| |
| with gr.Row(): |
| enable_amp = gr.Checkbox( |
| label=i18n("mixed_precision_amp"), |
| value=initial_settings.get("enable_amp", True), |
| info=i18n("mixed_precision_info") |
| ) |
| enable_tf32 = gr.Checkbox( |
| label=i18n("tf32_acceleration"), |
| value=initial_settings.get("enable_tf32", True), |
| info=i18n("tf32_acceleration_info") |
| ) |
| enable_cudnn_benchmark = gr.Checkbox( |
| label=i18n("cudnn_benchmark"), |
| value=initial_settings.get("enable_cudnn_benchmark", True), |
| info=i18n("cudnn_benchmark_info") |
| ) |
|
|
| with gr.Row(): |
| with gr.Column(scale=1): |
| use_tta = gr.Checkbox( |
| label=i18n("tta_boost"), |
| info=i18n("tta_info"), |
| value=initial_settings["use_tta"] |
| ) |
|
|
| with gr.Row(): |
| with gr.Column(scale=1): |
| use_demud_phaseremix_inst = gr.Checkbox( |
| label=i18n("phase_fix"), |
| info=i18n("phase_fix_info"), |
| value=initial_settings["use_demud_phaseremix_inst"] |
| ) |
|
|
| with gr.Column(scale=1): |
| extract_instrumental = gr.Checkbox( |
| label=i18n("instrumental"), |
| info=i18n("instrumental_info"), |
| value=initial_settings["extract_instrumental"] |
| ) |
|
|
| with gr.Row(): |
| use_apollo = gr.Checkbox( |
| label=i18n("enhance_with_apollo"), |
| value=initial_settings["use_apollo"], |
| info=i18n("apollo_enhancement_info") |
| ) |
|
|
| with gr.Group(visible=initial_settings["use_apollo"]) as apollo_settings_group: |
| with gr.Row(): |
| with gr.Column(scale=1): |
| apollo_chunk_size = gr.Slider( |
| label=i18n("apollo_chunk_size"), |
| minimum=3, |
| maximum=25, |
| step=1, |
| value=initial_settings["apollo_chunk_size"], |
| info=i18n("apollo_chunk_size_info"), |
| interactive=True |
| ) |
| with gr.Column(scale=1): |
| apollo_overlap = gr.Slider( |
| label=i18n("apollo_overlap"), |
| minimum=2, |
| maximum=10, |
| step=1, |
| value=initial_settings["apollo_overlap"], |
| info=i18n("apollo_overlap_info"), |
| interactive=True |
| ) |
|
|
| with gr.Row(): |
| apollo_method = gr.Dropdown( |
| label=i18n("apollo_processing_method"), |
| choices=[i18n("normal_method"), i18n("mid_side_method")], |
| value=i18n(initial_settings["apollo_method"]), |
| interactive=True |
| ) |
|
|
| with gr.Row(visible=initial_settings["apollo_method"] != "mid_side_method") as apollo_normal_model_row: |
| apollo_normal_model = gr.Dropdown( |
| label=i18n("apollo_normal_model"), |
| choices=["MP3 Enhancer", "Lew Vocal Enhancer", "Lew Vocal Enhancer v2 (beta)", "Apollo Universal Model"], |
| value=initial_settings["apollo_normal_model"], |
| interactive=True |
| ) |
|
|
| with gr.Row(visible=initial_settings["apollo_method"] == "mid_side_method") as apollo_midside_model_row: |
| apollo_midside_model = gr.Dropdown( |
| label=i18n("apollo_mid_side_model"), |
| choices=["MP3 Enhancer", "Lew Vocal Enhancer", "Lew Vocal Enhancer v2 (beta)", "Apollo Universal Model"], |
| value=initial_settings["apollo_midside_model"], |
| interactive=True |
| ) |
|
|
| with gr.Row(): |
| use_matchering = gr.Checkbox( |
| label=i18n("apply_matchering"), |
| value=initial_settings.get("use_matchering", False), |
| info=i18n("matchering_info") |
| ) |
|
|
| with gr.Group(visible=initial_settings.get("use_matchering", True)) as matchering_settings_group: |
| matchering_passes = gr.Slider( |
| label=i18n("matchering_passes"), |
| minimum=1, |
| maximum=5, |
| step=1, |
| value=initial_settings.get("matchering_passes", 1), |
| info=i18n("matchering_passes_info"), |
| interactive=True |
| ) |
|
|
| with gr.Row(): |
| process_btn = gr.Button(i18n("process"), variant="primary") |
| clear_old_output_btn = gr.Button(i18n("reset"), variant="secondary") |
| clear_old_output_status = gr.Textbox(label=i18n("status"), interactive=False) |
|
|
| |
| def update_favorite_button(model, favorites, cs_mode): |
| cleaned_model = clean_model(model) if model else None |
| is_favorited = cleaned_model in favorites if cleaned_model else False |
| fav_btn = gr.update(value=i18n("remove_favorite") if is_favorited else i18n("add_favorite")) |
| chunk_update = gr.update() |
| yaml_update = gr.update() |
| if cleaned_model: |
| native_chunk = get_model_chunk_size(cleaned_model) |
| if cs_mode == "base" and native_chunk and native_chunk in [352800, 485100]: |
| chunk_update = gr.update(value=native_chunk) |
| if cs_mode == "yaml": |
| if native_chunk: |
| yaml_update = gr.update(value=i18n("chunk_size_yaml_detected").format(native_chunk)) |
| else: |
| yaml_update = gr.update(value=i18n("chunk_size_yaml_not_downloaded")) |
| return fav_btn, chunk_update, yaml_update |
|
|
| def toggle_favorite(model, favorites): |
| if not model: |
| return favorites, gr.update(), gr.update() |
| cleaned_model = clean_model(model) |
| is_favorited = cleaned_model in favorites |
| new_favorites = update_favorites(favorites, cleaned_model, add=not is_favorited) |
| save_config(new_favorites, load_config()["settings"], load_config()["presets"]) |
| category = model_category.value |
| return ( |
| new_favorites, |
| gr.update(choices=update_model_dropdown(category, favorites=new_favorites)["choices"]), |
| gr.update(value=i18n("add_favorite") if is_favorited else i18n("remove_favorite")) |
| ) |
|
|
| def on_chunk_size_mode_change(mode, model): |
| cleaned = clean_model(model) if model else None |
| native_chunk = get_model_chunk_size(cleaned) if cleaned else None |
| yaml_text = ( |
| i18n("chunk_size_yaml_detected").format(native_chunk) |
| if native_chunk else i18n("chunk_size_yaml_not_downloaded") |
| ) |
| return ( |
| gr.update(visible=(mode == "base")), |
| gr.update(visible=(mode == "custom")), |
| gr.update(visible=(mode == "yaml"), value=yaml_text), |
| ) |
|
|
| chunk_size_mode.change( |
| fn=on_chunk_size_mode_change, |
| inputs=[chunk_size_mode, model_dropdown], |
| outputs=[chunk_size, chunk_size_custom, chunk_size_yaml_display] |
| ) |
|
|
| model_dropdown.change( |
| fn=update_favorite_button, |
| inputs=[model_dropdown, favorites_state, chunk_size_mode], |
| outputs=[favorite_button, chunk_size, chunk_size_yaml_display] |
| ) |
|
|
| favorite_button.click( |
| fn=toggle_favorite, |
| inputs=[model_dropdown, favorites_state], |
| outputs=[favorites_state, model_dropdown, favorite_button] |
| ) |
|
|
| use_apollo.change( |
| fn=lambda x: gr.update(visible=x), |
| inputs=use_apollo, |
| outputs=apollo_settings_group |
| ) |
|
|
| use_matchering.change( |
| fn=lambda x: gr.update(visible=x), |
| inputs=use_matchering, |
| outputs=matchering_settings_group |
| ) |
|
|
| apollo_method.change( |
| fn=lambda x: [ |
| gr.update(visible=x != i18n("mid_side_method")), |
| gr.update(visible=x == i18n("mid_side_method")), |
| "Apollo Universal Model" if x == i18n("mid_side_method") else None |
| ], |
| inputs=apollo_method, |
| outputs=[apollo_normal_model_row, apollo_midside_model_row, apollo_normal_model] |
| ) |
|
|
| with gr.Column(scale=2, min_width=800): |
| with gr.Tabs(): |
| with gr.Tab(i18n("main_tab")) as main_tab: |
| with gr.Column(): |
| original_audio = gr.Audio(label=i18n("original"), interactive=False) |
| with gr.Row(): |
| vocals_audio = gr.Audio(label=i18n("vocals")) |
| instrumental_audio = gr.Audio(label=i18n("instrumental_output")) |
| other_audio = gr.Audio(label=i18n("other")) |
|
|
| with gr.Tab(i18n("details_tab")) as details_tab: |
| with gr.Column(): |
| with gr.Row(): |
| male_audio = gr.Audio(label=i18n("male")) |
| female_audio = gr.Audio(label=i18n("female")) |
| speech_audio = gr.Audio(label=i18n("speech")) |
| with gr.Row(): |
| drum_audio = gr.Audio(label=i18n("drums")) |
| bass_audio = gr.Audio(label=i18n("bass")) |
| with gr.Row(): |
| effects_audio = gr.Audio(label=i18n("effects")) |
|
|
| with gr.Tab(i18n("advanced_tab")) as advanced_tab: |
| with gr.Column(): |
| with gr.Row(): |
| phaseremix_audio = gr.Audio(label=i18n("phase_remix")) |
| dry_audio = gr.Audio(label=i18n("dry")) |
| with gr.Row(): |
| music_audio = gr.Audio(label=i18n("music")) |
| karaoke_audio = gr.Audio(label=i18n("karaoke")) |
| bleed_audio = gr.Audio(label=i18n("bleed")) |
| with gr.Row(): |
| mid_audio = gr.Audio(label="Mid") |
| side_audio = gr.Audio(label="Side") |
|
|
| separation_progress_html = gr.HTML( |
| value=f""" |
| <div id="custom-progress" style="margin-top: 10px;"> |
| <div style="font-size: 1rem; color: #C0C0C0; margin-bottom: 5px;" id="progress-label">{i18n("waiting_for_processing")}</div> |
| <div style="width: 100%; background-color: #444; border-radius: 5px; overflow: hidden;"> |
| <div id="progress-bar" style="width: 0%; height: 20px; background-color: #6e8efb; transition: width 0.3s;"></div> |
| </div> |
| </div> |
| """ |
| ) |
| separation_process_status = gr.Textbox( |
| label=i18n("status"), |
| interactive=False, |
| placeholder=i18n("waiting_for_processing"), |
| visible=False |
| ) |
| processing_tip = gr.Markdown(i18n("processing_tip")) |
|
|
| with gr.Tab(i18n("auto_ensemble_tab"), id="auto_ensemble_tab"): |
| with gr.Row(): |
| with gr.Column(): |
| with gr.Group(): |
| auto_input_audio_file = gr.File( |
| file_types=[".wav", ".mp3", ".m4a", ".mp4", ".mkv", ".flac"], |
| label=i18n("upload_file") |
| ) |
| auto_file_path_input = gr.Textbox( |
| label=i18n("enter_file_path"), |
| placeholder=i18n("file_path_placeholder"), |
| interactive=True |
| ) |
|
|
| with gr.Accordion(i18n("advanced_settings"), open=False) as auto_settings_accordion: |
| with gr.Row(): |
| auto_use_tta = gr.Checkbox(label=i18n("use_tta"), value=False) |
| auto_extract_instrumental = gr.Checkbox(label=i18n("instrumental_only")) |
|
|
| with gr.Row(): |
| auto_overlap = gr.Slider( |
| label=i18n("auto_overlap"), |
| minimum=2, |
| maximum=50, |
| value=2, |
| step=1 |
| ) |
| auto_chunk_size = gr.Dropdown( |
| label=i18n("auto_chunk_size"), |
| choices=[352800, 485100], |
| value=352800 |
| ) |
| export_format2 = gr.Dropdown( |
| label=i18n("output_format"), |
| choices=['wav FLOAT', 'flac PCM_16', 'flac PCM_24'], |
| value='wav FLOAT' |
| ) |
|
|
| with gr.Row(): |
| auto_use_apollo = gr.Checkbox( |
| label=i18n("enhance_with_apollo"), |
| value=False, |
| info=i18n("apollo_enhancement_info") |
| ) |
|
|
| with gr.Group(visible=False) as auto_apollo_settings_group: |
| with gr.Row(): |
| with gr.Column(scale=1): |
| auto_apollo_chunk_size = gr.Slider( |
| label=i18n("apollo_chunk_size"), |
| minimum=3, |
| maximum=25, |
| step=1, |
| value=19, |
| info=i18n("apollo_chunk_size_info"), |
| interactive=True |
| ) |
| with gr.Column(scale=1): |
| auto_apollo_overlap = gr.Slider( |
| label=i18n("apollo_overlap"), |
| minimum=2, |
| maximum=10, |
| step=1, |
| value=2, |
| info=i18n("apollo_overlap_info"), |
| interactive=True |
| ) |
|
|
| with gr.Row(): |
| auto_apollo_method = gr.Dropdown( |
| label=i18n("apollo_processing_method"), |
| choices=[i18n("normal_method"), i18n("mid_side_method")], |
| value=i18n("normal_method"), |
| interactive=True |
| ) |
|
|
| with gr.Row(visible=True) as auto_apollo_normal_model_row: |
| auto_apollo_normal_model = gr.Dropdown( |
| label=i18n("apollo_normal_model"), |
| choices=["MP3 Enhancer", "Lew Vocal Enhancer", "Lew Vocal Enhancer v2 (beta)", "Apollo Universal Model"], |
| value="Apollo Universal Model", |
| interactive=True |
| ) |
|
|
| with gr.Row(visible=False) as auto_apollo_midside_model_row: |
| auto_apollo_midside_model = gr.Dropdown( |
| label=i18n("apollo_mid_side_model"), |
| choices=["MP3 Enhancer", "Lew Vocal Enhancer", "Lew Vocal Enhancer v2 (beta)", "Apollo Universal Model"], |
| value="Apollo Universal Model", |
| interactive=True |
| ) |
|
|
| with gr.Row(): |
| auto_use_matchering = gr.Checkbox( |
| label=i18n("apply_matchering"), |
| value=False, |
| info=i18n("matchering_info") |
| ) |
|
|
| with gr.Group(visible=True) as auto_matchering_settings_group: |
| auto_matchering_passes = gr.Slider( |
| label=i18n("matchering_passes"), |
| minimum=1, |
| maximum=5, |
| step=1, |
| value=1, |
| info=i18n("matchering_passes_info"), |
| interactive=True |
| ) |
|
|
| with gr.Group(): |
| model_selection_header = gr.Markdown(f"### {i18n('model_selection')}") |
| with gr.Row(): |
| auto_category_dropdown = gr.Dropdown( |
| label=i18n("model_category"), |
| choices=[i18n(cat) for cat in get_all_model_configs_with_custom().keys()], |
| value=i18n("Vocal Models") |
| ) |
| selected_models = gr.Dropdown( |
| label=i18n("selected_models"), |
| choices=update_model_dropdown(i18n(initial_settings["auto_category"]), favorites=initial_favorites)["choices"], |
| value=initial_settings["selected_models"], |
| multiselect=True |
| ) |
|
|
| with gr.Row(): |
| preset_dropdown = gr.Dropdown( |
| label=i18n("select_preset"), |
| choices=list(initial_presets.keys()), |
| value=None, |
| allow_custom_value=False, |
| interactive=True |
| ) |
| with gr.Row(): |
| preset_name_input = gr.Textbox( |
| label=i18n("preset_name"), |
| placeholder=i18n("enter_preset_name"), |
| interactive=True |
| ) |
| save_preset_btn = gr.Button(i18n("save_preset"), variant="secondary", scale=0) |
| delete_preset_btn = gr.Button(i18n("delete_preset"), variant="secondary", scale=0) |
| refresh_presets_btn = gr.Button(i18n("refresh_presets"), variant="secondary", scale=0) |
|
|
| with gr.Group(): |
| ensemble_settings_header = gr.Markdown(f"### {i18n('ensemble_settings')}") |
| with gr.Row(): |
| auto_ensemble_type = gr.Dropdown( |
| label=i18n("method"), |
| choices=['avg_wave', 'median_wave', 'min_wave', 'max_wave', |
| 'avg_fft', 'median_fft', 'min_fft', 'max_fft'], |
| value=initial_settings["auto_ensemble_type"] |
| ) |
|
|
| ensemble_recommendation = gr.Markdown(i18n("recommendation")) |
|
|
| auto_process_btn = gr.Button(i18n("start_processing"), variant="primary") |
|
|
| def load_preset(preset_name, presets, category, favorites): |
| if preset_name and preset_name in presets: |
| preset = presets[preset_name] |
| |
| favorite_models = [f"{model} ⭐" if model in favorites else model for model in preset["models"]] |
| |
| preset_category = preset.get("auto_category_dropdown", category) |
| |
| model_choices = update_model_dropdown(preset_category, favorites=favorites)["choices"] |
| return ( |
| gr.update(value=preset_category), |
| gr.update(choices=model_choices, value=favorite_models), |
| gr.update(value=preset["ensemble_method"]) |
| ) |
| return gr.update(), gr.update(), gr.update() |
|
|
| def sync_presets(): |
| """Reload presets from config and update dropdown.""" |
| config = load_config() |
| return config["presets"], gr.update(choices=list(config["presets"].keys()), value=None) |
|
|
| preset_dropdown.change( |
| fn=load_preset, |
| inputs=[preset_dropdown, presets_state, auto_category_dropdown, favorites_state], |
| outputs=[auto_category_dropdown, selected_models, auto_ensemble_type] |
| ) |
|
|
| def handle_save_preset(preset_name, models, ensemble_method, presets, favorites, auto_category_dropdown): |
| if not preset_name: |
| return gr.update(), presets, i18n("no_preset_name_provided") |
| if not models and not favorites: |
| return gr.update(), presets, i18n("no_models_selected_for_preset") |
| new_presets = save_preset( |
| presets, |
| preset_name, |
| models, |
| ensemble_method, |
| auto_category_dropdown=auto_category_dropdown |
| ) |
| save_config(favorites, load_config()["settings"], new_presets) |
| return gr.update(choices=list(new_presets.keys()), value=None), new_presets, i18n("preset_saved").format(preset_name) |
|
|
| save_preset_btn.click( |
| fn=handle_save_preset, |
| inputs=[preset_name_input, selected_models, auto_ensemble_type, presets_state, favorites_state, auto_category_dropdown], |
| outputs=[preset_dropdown, presets_state] |
| ) |
|
|
| def handle_delete_preset(preset_name, presets): |
| if not preset_name or preset_name not in presets: |
| return gr.update(), presets |
| new_presets = delete_preset(presets, preset_name) |
| save_config(load_config()["favorites"], load_config()["settings"], new_presets) |
| return gr.update(choices=list(new_presets.keys()), value=None), new_presets |
|
|
| delete_preset_btn.click( |
| fn=handle_delete_preset, |
| inputs=[preset_dropdown, presets_state], |
| outputs=[preset_dropdown, presets_state] |
| ) |
|
|
| refresh_presets_btn.click( |
| fn=sync_presets, |
| inputs=[], |
| outputs=[presets_state, preset_dropdown] |
| ) |
|
|
| auto_use_apollo.change( |
| fn=lambda x: gr.update(visible=x), |
| inputs=auto_use_apollo, |
| outputs=auto_apollo_settings_group |
| ) |
|
|
| auto_use_matchering.change( |
| fn=lambda x: gr.update(visible=x), |
| inputs=auto_use_matchering, |
| outputs=auto_matchering_settings_group |
| ) |
|
|
| auto_apollo_method.change( |
| fn=lambda x: [ |
| gr.update(visible=x != i18n("mid_side_method")), |
| gr.update(visible=x == i18n("mid_side_method")), |
| "Apollo Universal Model" if x == i18n("mid_side_method") else None |
| ], |
| inputs=auto_apollo_method, |
| outputs=[auto_apollo_normal_model_row, auto_apollo_midside_model_row, auto_apollo_normal_model] |
| ) |
|
|
| with gr.Column(): |
| with gr.Tabs(): |
| with gr.Tab(i18n("original_audio_tab")) as original_audio_tab: |
| original_audio2 = gr.Audio( |
| label=i18n("original_audio"), |
| interactive=False, |
| every=1, |
| elem_id="original_audio_player", |
| streaming=True |
| ) |
| with gr.Tab(i18n("ensemble_result_tab")) as ensemble_result_tab: |
| auto_output_audio = gr.Audio( |
| label=i18n("output_preview"), |
| interactive=False, |
| streaming=True |
| ) |
| refresh_output_btn = gr.Button(i18n("refresh_output"), variant="secondary") |
|
|
| ensemble_progress_html = gr.HTML( |
| value=f""" |
| <div id="custom-progress" style="margin-top: 10px;"> |
| <div style="font-size: 1rem; color: #C0C0C0; margin-bottom: 5px;" id="progress-label">{i18n("waiting_for_processing")}</div> |
| <div style="width: 100%; background-color: #444; border-radius: 5px; overflow: hidden;"> |
| <div id="progress-bar" style="width: 0%; height: 20px; background-color: #6e8efb; transition: width 0.3s;"></div> |
| </div> |
| </div> |
| """ |
| ) |
| ensemble_process_status = gr.Textbox( |
| label=i18n("status"), |
| interactive=False, |
| placeholder=i18n("waiting_for_processing"), |
| visible=False |
| ) |
| |
| with gr.Tab(i18n("download_sources_tab"), id="download_tab"): |
| with gr.Row(): |
| with gr.Column(): |
| gr.Markdown(f"### {i18n('direct_links')}") |
| direct_url_input = gr.Textbox(label=i18n("audio_file_url")) |
| direct_download_btn = gr.Button(i18n("download_from_url"), variant="secondary") |
| direct_download_status = gr.Textbox(label=i18n("download_status")) |
| direct_download_output = gr.File(label=i18n("downloaded_file"), interactive=False) |
|
|
| with gr.Column(): |
| gr.Markdown(f"### {i18n('cookie_management')}") |
| cookie_file = gr.File( |
| label=i18n("upload_cookies_txt"), |
| file_types=[".txt"], |
| interactive=True, |
| elem_id="cookie_upload" |
| ) |
| cookie_info = gr.Markdown(i18n("cookie_info")) |
|
|
| with gr.Tab(i18n("manual_ensemble_tab"), id="manual_ensemble_tab"): |
| with gr.Row(equal_height=True): |
| with gr.Column(scale=1, min_width=400): |
| with gr.Accordion(i18n("input_sources"), open=True) as input_sources_accordion: |
| with gr.Row(): |
| refresh_btn = gr.Button(i18n("refresh"), variant="secondary", size="sm") |
| ensemble_type = gr.Dropdown( |
| label=i18n("ensemble_algorithm"), |
| choices=['avg_wave', 'median_wave', 'min_wave', 'max_wave', |
| 'avg_fft', 'median_fft', 'min_fft', 'max_fft'], |
| value='avg_wave' |
| ) |
|
|
| file_dropdown_header = gr.Markdown(f"### {i18n('select_audio_files')}") |
| file_path = os.path.join(Path.home(), 'Music-Source-Separation', 'output') |
| initial_files = glob.glob(f"{file_path}/*.wav") + glob.glob(os.path.join(BASE_DIR, 'Music-Source-Separation-Training', 'old_output', '*.wav')) |
| file_dropdown = gr.Dropdown( |
| choices=initial_files, |
| label=i18n("available_files"), |
| multiselect=True, |
| interactive=True, |
| elem_id="file-dropdown" |
| ) |
| weights_input = gr.Textbox( |
| label=i18n("custom_weights"), |
| placeholder=i18n("custom_weights_placeholder"), |
| info=i18n("custom_weights_info") |
| ) |
|
|
| with gr.Column(scale=2, min_width=800): |
| with gr.Tabs(): |
| with gr.Tab(i18n("result_preview_tab")) as result_preview_tab: |
| ensemble_output_audio = gr.Audio( |
| label=i18n("ensembled_output"), |
| interactive=False, |
| elem_id="output-audio", |
| streaming=True |
| ) |
| with gr.Tab(i18n("processing_log_tab")) as processing_log_tab: |
| with gr.Accordion(i18n("processing_details"), open=True, elem_id="log-accordion"): |
| ensemble_status = gr.Textbox( |
| label="", |
| interactive=False, |
| placeholder=i18n("processing_log_placeholder"), |
| lines=10, |
| max_lines=20, |
| elem_id="log-box" |
| ) |
| with gr.Row(): |
| ensemble_process_btn = gr.Button( |
| i18n("process_ensemble"), |
| variant="primary", |
| size="sm", |
| elem_id="process-btn" |
| ) |
|
|
| with gr.Tab(i18n("phase_fixer_tab"), id="phase_fixer_tab"): |
| with gr.Row(equal_height=True): |
| with gr.Column(scale=1, min_width=350): |
| with gr.Group(): |
| with gr.Row(): |
| pf_source_file = gr.File( |
| file_types=[".wav", ".flac", ".mp3"], |
| label=i18n("source_file_label") |
| ) |
| pf_target_file = gr.File( |
| file_types=[".wav", ".flac", ".mp3"], |
| label=i18n("target_file_label") |
| ) |
| |
| with gr.Group(): |
| with gr.Row(): |
| pf_source_model = gr.Dropdown( |
| label=i18n("source_model"), |
| choices=SOURCE_MODELS, |
| value=SOURCE_MODELS[0], |
| info=i18n("source_model_info") |
| ) |
| with gr.Row(): |
| pf_target_model = gr.Dropdown( |
| label=i18n("target_model"), |
| choices=TARGET_MODELS, |
| value=TARGET_MODELS[-1], |
| info=i18n("target_model_info") |
| ) |
| |
| with gr.Accordion(i18n("phase_fixer_settings"), open=False): |
| with gr.Row(): |
| pf_scale_factor = gr.Slider( |
| label=i18n("scale_factor"), |
| minimum=0.5, |
| maximum=3.0, |
| step=0.05, |
| value=1.4, |
| info=i18n("scale_factor_info") |
| ) |
| pf_output_format = gr.Dropdown( |
| label=i18n("output_format"), |
| choices=['flac', 'wav'], |
| value='flac' |
| ) |
| |
| with gr.Row(): |
| pf_low_cutoff = gr.Slider( |
| label=i18n("low_cutoff"), |
| minimum=100, |
| maximum=2000, |
| step=100, |
| value=500, |
| info=i18n("low_cutoff_info") |
| ) |
| pf_high_cutoff = gr.Slider( |
| label=i18n("high_cutoff"), |
| minimum=2000, |
| maximum=15000, |
| step=500, |
| value=9000, |
| info=i18n("high_cutoff_info") |
| ) |
| |
| pf_process_btn = gr.Button(i18n("run_phase_fixer"), variant="primary") |
| |
| with gr.Column(scale=2, min_width=600): |
| pf_output_audio = gr.Audio( |
| label=i18n("phase_fixed_output"), |
| interactive=False, |
| streaming=True |
| ) |
| pf_status = gr.Textbox( |
| label=i18n("status"), |
| interactive=False, |
| placeholder=i18n("waiting_for_processing"), |
| lines=2 |
| ) |
|
|
| from phase_fixer import process_phase_fix |
| |
| def run_phase_fixer(source_file, target_file, source_model, target_model, scale_factor, low_cutoff, high_cutoff, output_format): |
| if source_file is None or target_file is None: |
| return None, i18n("please_upload_both_files") |
| |
| source_path = source_file.name if hasattr(source_file, 'name') else source_file |
| target_path = target_file.name if hasattr(target_file, 'name') else target_file |
| |
| output_folder = os.path.join(BASE_DIR, 'phase_fixer_output') |
| |
| output_file, status = process_phase_fix( |
| source_file=source_path, |
| target_file=target_path, |
| output_folder=output_folder, |
| low_cutoff=int(low_cutoff), |
| high_cutoff=int(high_cutoff), |
| scale_factor=float(scale_factor), |
| output_format=output_format |
| ) |
| |
| return output_file, status |
| |
| pf_process_btn.click( |
| fn=run_phase_fixer, |
| inputs=[pf_source_file, pf_target_file, pf_source_model, pf_target_model, pf_scale_factor, pf_low_cutoff, pf_high_cutoff, pf_output_format], |
| outputs=[pf_output_audio, pf_status] |
| ) |
|
|
| with gr.Tab(i18n("batch_processing_tab"), id="batch_processing_tab"): |
| with gr.Row(equal_height=True): |
| with gr.Column(scale=1, min_width=350): |
| gr.Markdown(f"### {i18n('batch_description')}") |
| |
| with gr.Group(): |
| batch_input_files = gr.File( |
| file_types=[".wav", ".mp3", ".m4a", ".flac"], |
| file_count="multiple", |
| label=i18n("batch_add_files") |
| ) |
| batch_input_folder = gr.Textbox( |
| label=i18n("batch_input_folder"), |
| placeholder=i18n("batch_input_folder_placeholder") |
| ) |
| batch_output_folder = gr.Textbox( |
| label=i18n("batch_output_folder"), |
| placeholder=i18n("batch_output_folder_placeholder"), |
| value=os.path.join(BASE_DIR, "batch_output") |
| ) |
| |
| with gr.Group(): |
| batch_model_category = gr.Dropdown( |
| label=i18n("model_category"), |
| choices=[i18n(cat) for cat in get_all_model_configs_with_custom().keys()], |
| value=i18n("Vocal Models") |
| ) |
| batch_model_dropdown = gr.Dropdown( |
| label=i18n("model"), |
| choices=update_model_dropdown(i18n("Vocal Models"), favorites=initial_favorites)["choices"], |
| value=None |
| ) |
| |
| with gr.Accordion(i18n("settings"), open=False): |
| with gr.Row(): |
| batch_chunk_size = gr.Dropdown( |
| label=i18n("chunk_size"), |
| choices=[352800, 485100], |
| value=352800 |
| ) |
| batch_overlap = gr.Slider( |
| minimum=2, |
| maximum=50, |
| step=1, |
| label=i18n("overlap"), |
| value=2 |
| ) |
| with gr.Row(): |
| batch_export_format = gr.Dropdown( |
| label=i18n("format"), |
| choices=['wav FLOAT', 'flac PCM_16', 'flac PCM_24'], |
| value='wav FLOAT' |
| ) |
| batch_extract_instrumental = gr.Checkbox( |
| label=i18n("instrumental"), |
| value=True |
| ) |
| |
| with gr.Row(): |
| batch_start_btn = gr.Button(i18n("batch_start"), variant="primary") |
| batch_stop_btn = gr.Button(i18n("batch_stop"), variant="secondary") |
| |
| with gr.Column(scale=2, min_width=600): |
| batch_file_list = gr.Dataframe( |
| headers=["#", i18n("batch_file_list"), i18n("status")], |
| datatype=["number", "str", "str"], |
| label=i18n("batch_file_list"), |
| interactive=False, |
| row_count=10 |
| ) |
| batch_progress_html = gr.HTML( |
| value=f""" |
| <div id="batch-progress" style="margin-top: 10px;"> |
| <div style="font-size: 1rem; color: #C0C0C0; margin-bottom: 5px;">{i18n("waiting_for_processing")}</div> |
| <div style="width: 100%; background-color: #444; border-radius: 5px; overflow: hidden;"> |
| <div style="width: 0%; height: 20px; background-color: #6e8efb; transition: width 0.3s;"></div> |
| </div> |
| </div> |
| """ |
| ) |
| batch_status = gr.Textbox( |
| label=i18n("status"), |
| interactive=False, |
| placeholder=i18n("waiting_for_processing"), |
| lines=3 |
| ) |
| |
| |
| batch_stop_flag = gr.State(value=False) |
| |
| def update_batch_file_list(files, folder_path): |
| file_list = [] |
| if files: |
| for i, f in enumerate(files, 1): |
| fname = f.name if hasattr(f, 'name') else str(f) |
| file_list.append([i, os.path.basename(fname), "⏳ Pending"]) |
| if folder_path and os.path.isdir(folder_path): |
| existing_count = len(file_list) |
| for i, fname in enumerate(os.listdir(folder_path), existing_count + 1): |
| if fname.lower().endswith(('.wav', '.mp3', '.m4a', '.flac')): |
| file_list.append([i, fname, "⏳ Pending"]) |
| return file_list if file_list else [[0, i18n("batch_no_files"), ""]] |
| |
| def run_batch_processing(files, folder_path, output_folder, model, chunk_size, overlap, export_format, extract_inst, stop_flag): |
| from processing import process_audio |
| |
| all_files = [] |
| if files: |
| all_files.extend([f.name if hasattr(f, 'name') else str(f) for f in files]) |
| if folder_path and os.path.isdir(folder_path): |
| for fname in os.listdir(folder_path): |
| if fname.lower().endswith(('.wav', '.mp3', '.m4a', '.flac')): |
| all_files.append(os.path.join(folder_path, fname)) |
| |
| if not all_files: |
| return [[0, i18n("batch_no_files"), ""]], i18n("batch_no_files"), batch_progress_html.value |
| |
| os.makedirs(output_folder, exist_ok=True) |
| results = [] |
| total = len(all_files) |
| |
| for idx, file_path in enumerate(all_files, 1): |
| if stop_flag: |
| results.append([idx, os.path.basename(file_path), "Stopped"]) |
| continue |
| |
| results.append([idx, os.path.basename(file_path), "🔄 Processing..."]) |
| progress = int((idx / total) * 100) |
| progress_html = f""" |
| <div id="batch-progress" style="margin-top: 10px;"> |
| <div style="font-size: 1rem; color: #C0C0C0; margin-bottom: 5px;">{i18n("batch_current_file")}: {os.path.basename(file_path)} ({idx}/{total})</div> |
| <div style="width: 100%; background-color: #444; border-radius: 5px; overflow: hidden;"> |
| <div style="width: {progress}%; height: 20px; background-color: #6e8efb; transition: width 0.3s;"></div> |
| </div> |
| </div> |
| """ |
| |
| try: |
| |
| results[-1][2] = "Done" |
| except Exception as e: |
| results[-1][2] = f"Error: {str(e)[:30]}" |
| |
| final_status = i18n("batch_stopped") if stop_flag else i18n("batch_completed") |
| return results, final_status, progress_html |
| |
| batch_input_files.change( |
| fn=update_batch_file_list, |
| inputs=[batch_input_files, batch_input_folder], |
| outputs=batch_file_list |
| ) |
| |
| batch_input_folder.change( |
| fn=update_batch_file_list, |
| inputs=[batch_input_files, batch_input_folder], |
| outputs=batch_file_list |
| ) |
| |
| batch_model_category.change( |
| fn=lambda cat: gr.update(choices=update_model_dropdown(next((k for k in get_all_model_configs_with_custom().keys() if i18n(k) == cat), list(get_all_model_configs_with_custom().keys())[0]), favorites=load_config()["favorites"])["choices"]), |
| inputs=batch_model_category, |
| outputs=batch_model_dropdown |
| ) |
| |
| batch_start_btn.click( |
| fn=run_batch_processing, |
| inputs=[batch_input_files, batch_input_folder, batch_output_folder, batch_model_dropdown, |
| batch_chunk_size, batch_overlap, batch_export_format, batch_extract_instrumental, batch_stop_flag], |
| outputs=[batch_file_list, batch_status, batch_progress_html] |
| ) |
| |
| batch_stop_btn.click( |
| fn=lambda: True, |
| outputs=batch_stop_flag |
| ) |
|
|
| with gr.Tab(i18n("custom_models_tab"), id="custom_models_tab"): |
| with gr.Row(equal_height=True): |
| with gr.Column(scale=1, min_width=400): |
| gr.Markdown(f"### {i18n('add_custom_model')}") |
| gr.Markdown(i18n("custom_model_info")) |
| |
| with gr.Group(): |
| custom_model_name_input = gr.Textbox( |
| label=i18n("custom_model_name"), |
| placeholder=i18n("custom_model_name_placeholder"), |
| interactive=True |
| ) |
| custom_checkpoint_url = gr.Textbox( |
| label=i18n("checkpoint_url"), |
| placeholder=i18n("checkpoint_url_placeholder"), |
| interactive=True |
| ) |
| custom_config_url = gr.Textbox( |
| label=i18n("config_url"), |
| placeholder=i18n("config_url_placeholder"), |
| interactive=True |
| ) |
| custom_py_url = gr.Textbox( |
| label=i18n("custom_py_url"), |
| placeholder=i18n("custom_py_url_placeholder"), |
| interactive=True |
| ) |
| |
| with gr.Row(): |
| auto_detect_checkbox = gr.Checkbox( |
| label=i18n("auto_detect_type"), |
| value=True, |
| interactive=True |
| ) |
| custom_model_type = gr.Dropdown( |
| label=i18n("model_type"), |
| choices=SUPPORTED_MODEL_TYPES, |
| value="bs_roformer", |
| interactive=True, |
| visible=False |
| ) |
| |
| add_model_btn = gr.Button(i18n("add_model_btn"), variant="primary") |
| add_model_status = gr.Textbox(label=i18n("status"), interactive=False) |
| |
| with gr.Column(scale=1, min_width=400): |
| gr.Markdown(f"### {i18n('custom_models_list')}") |
| |
| custom_models_list_display = gr.Dataframe( |
| headers=[i18n("custom_model_name"), i18n("model_type")], |
| datatype=["str", "str"], |
| label="", |
| interactive=False, |
| row_count=10 |
| ) |
| |
| with gr.Row(): |
| delete_model_dropdown = gr.Dropdown( |
| label=i18n("select_model_to_delete"), |
| choices=[], |
| interactive=True |
| ) |
| delete_model_btn = gr.Button(i18n("delete_model"), variant="secondary") |
| |
| refresh_custom_models_btn = gr.Button(i18n("refresh_models"), variant="secondary") |
| delete_model_status = gr.Textbox(label=i18n("status"), interactive=False) |
| |
| |
| def toggle_model_type_visibility(auto_detect): |
| return gr.update(visible=not auto_detect) |
| |
| def refresh_custom_models_display(): |
| models_list = get_custom_models_list() |
| if not models_list: |
| return [[i18n("no_custom_models"), ""]], gr.update(choices=[]) |
| data = [[name, mtype] for name, mtype in models_list] |
| choices = [name for name, _ in models_list] |
| return data, gr.update(choices=choices) |
| |
| def add_model_handler(name, checkpoint_url, config_url, py_url, auto_detect, model_type): |
| selected_type = "auto" if auto_detect else model_type |
| success, message = add_custom_model(name, selected_type, checkpoint_url, config_url, py_url, auto_detect) |
| if success: |
| |
| models_list = get_custom_models_list() |
| data = [[n, t] for n, t in models_list] if models_list else [[i18n("no_custom_models"), ""]] |
| choices = [n for n, _ in models_list] if models_list else [] |
| |
| all_configs = get_all_model_configs_with_custom() |
| category_choices = [i18n(cat) for cat in all_configs.keys()] |
| return ( |
| i18n("model_added_success"), |
| data, |
| gr.update(choices=choices), |
| gr.update(choices=category_choices), |
| gr.update(choices=category_choices), |
| gr.update(choices=category_choices), |
| "", "", "", "" |
| ) |
| return ( |
| i18n("model_add_error").format(message), |
| gr.update(), |
| gr.update(), |
| gr.update(), |
| gr.update(), |
| gr.update(), |
| gr.update(), gr.update(), gr.update(), gr.update() |
| ) |
| |
| def delete_model_handler(model_name): |
| if not model_name: |
| return i18n("select_model_to_delete"), gr.update(), gr.update() |
| success, message = delete_custom_model(model_name) |
| if success: |
| models_list = get_custom_models_list() |
| data = [[n, t] for n, t in models_list] if models_list else [[i18n("no_custom_models"), ""]] |
| choices = [n for n, _ in models_list] if models_list else [] |
| |
| all_configs = get_all_model_configs_with_custom() |
| category_choices = [i18n(cat) for cat in all_configs.keys()] |
| return ( |
| i18n("model_deleted_success"), |
| data, |
| gr.update(choices=choices, value=None), |
| gr.update(choices=category_choices), |
| gr.update(choices=category_choices), |
| gr.update(choices=category_choices) |
| ) |
| return i18n("model_delete_error").format(message), gr.update(), gr.update(), gr.update(), gr.update(), gr.update() |
| |
| |
| auto_detect_checkbox.change( |
| fn=toggle_model_type_visibility, |
| inputs=auto_detect_checkbox, |
| outputs=custom_model_type |
| ) |
| |
| add_model_btn.click( |
| fn=add_model_handler, |
| inputs=[custom_model_name_input, custom_checkpoint_url, custom_config_url, custom_py_url, auto_detect_checkbox, custom_model_type], |
| outputs=[add_model_status, custom_models_list_display, delete_model_dropdown, model_category, auto_category_dropdown, batch_model_category, custom_model_name_input, custom_checkpoint_url, custom_config_url, custom_py_url] |
| ) |
| |
| delete_model_btn.click( |
| fn=delete_model_handler, |
| inputs=delete_model_dropdown, |
| outputs=[delete_model_status, custom_models_list_display, delete_model_dropdown, model_category, auto_category_dropdown, batch_model_category] |
| ) |
| |
| refresh_custom_models_btn.click( |
| fn=refresh_custom_models_display, |
| outputs=[custom_models_list_display, delete_model_dropdown] |
| ) |
| |
| |
| demo.load( |
| fn=refresh_custom_models_display, |
| outputs=[custom_models_list_display, delete_model_dropdown] |
| ) |
|
|
| def save_settings_on_process(*args): |
| """Generator function that forwards progress yields from process_audio.""" |
| apollo_method_value = args[15] |
| backend_apollo_method = "mid_side_method" if apollo_method_value == i18n("mid_side_method") else "normal_method" |
| cleaned_model = clean_model(args[1]) if args[1] else None |
|
|
| |
| |
| cs_mode = args[22] if len(args) > 22 else "base" |
| cs_custom_val = args[23] if len(args) > 23 else 352800 |
| cs_base_val = args[2] |
|
|
| if cs_mode == "custom": |
| effective_chunk = int(cs_custom_val) if cs_custom_val else 352800 |
| elif cs_mode == "yaml": |
| effective_chunk = "yaml" |
| else: |
| effective_chunk = int(cs_base_val) if cs_base_val else 352800 |
|
|
| settings = { |
| "chunk_size": cs_base_val, |
| "chunk_size_mode": cs_mode, |
| "chunk_size_custom": cs_custom_val, |
| "overlap": args[3], |
| "export_format": args[4], |
| "optimize_mode": args[5], |
| "enable_amp": args[6], |
| "enable_tf32": args[7], |
| "enable_cudnn_benchmark": args[8], |
| "use_tta": args[9], |
| "use_demud_phaseremix_inst": args[10], |
| "extract_instrumental": args[11], |
| "use_apollo": args[12], |
| "apollo_chunk_size": args[13], |
| "apollo_overlap": args[14], |
| "apollo_method": backend_apollo_method, |
| "apollo_normal_model": args[16], |
| "apollo_midside_model": args[17], |
| "use_matchering": args[18], |
| "matchering_passes": args[19], |
| "model_category": args[20], |
| "selected_model": cleaned_model, |
| "auto_ensemble_type": args[11] |
| } |
| save_config(load_config()["favorites"], settings, load_config()["presets"]) |
| |
| modified_args = list(args[:22]) |
| modified_args[1] = cleaned_model |
| modified_args[2] = effective_chunk |
| modified_args[21] = cleaned_model |
| |
| for update in process_audio(*modified_args): |
| yield update |
|
|
| def save_auto_ensemble_settings(*args): |
| """Generator function that forwards progress yields from auto_ensemble_process.""" |
| settings = load_config()["settings"] |
| settings["auto_ensemble_type"] = args[7] |
| settings["use_matchering"] = args[14] |
| settings["matchering_passes"] = args[15] |
| save_config(load_config()["favorites"], settings, load_config()["presets"]) |
| |
| for update in auto_ensemble_process(*args): |
| if isinstance(update, tuple) and len(update) == 3: |
| yield update |
|
|
| def update_category_dropdowns(cat): |
| all_configs = get_all_model_configs_with_custom() |
| eng_cat = next((k for k in all_configs.keys() if i18n(k) == cat), list(all_configs.keys())[0]) |
| choices = update_model_dropdown(eng_cat, favorites=load_config()["favorites"])["choices"] |
| return gr.update(choices=choices), gr.update(choices=choices) |
|
|
| model_category.change( |
| fn=update_category_dropdowns, |
| inputs=model_category, |
| outputs=[model_dropdown, selected_models] |
| ) |
|
|
| clear_old_output_btn.click(fn=clear_old_output, outputs=clear_old_output_status) |
|
|
| input_audio_file.upload( |
| fn=lambda x, y: handle_file_upload(x, y, is_auto_ensemble=False), |
| inputs=[input_audio_file, file_path_input], |
| outputs=[input_audio_file, original_audio] |
| ) |
| file_path_input.change( |
| fn=lambda x, y: handle_file_upload(x, y, is_auto_ensemble=False), |
| inputs=[input_audio_file, file_path_input], |
| outputs=[input_audio_file, original_audio] |
| ) |
|
|
| auto_input_audio_file.upload( |
| fn=lambda x, y: handle_file_upload(x, y, is_auto_ensemble=True), |
| inputs=[auto_input_audio_file, auto_file_path_input], |
| outputs=[auto_input_audio_file, original_audio2] |
| ) |
| auto_file_path_input.change( |
| fn=lambda x, y: handle_file_upload(x, y, is_auto_ensemble=True), |
| inputs=[auto_input_audio_file, auto_file_path_input], |
| outputs=[auto_input_audio_file, original_audio2] |
| ) |
|
|
| auto_category_dropdown.change( |
| fn=lambda cat: gr.update(choices=update_model_dropdown(next((k for k in get_all_model_configs_with_custom().keys() if i18n(k) == cat), list(get_all_model_configs_with_custom().keys())[0]), favorites=load_config()["favorites"])["choices"]), |
| inputs=auto_category_dropdown, |
| outputs=selected_models |
| ) |
|
|
| def clean_inputs(*args): |
| cleaned_args = list(args) |
| cleaned_args[1] = clean_model(cleaned_args[1]) if cleaned_args[1] else None |
| cleaned_args[21] = clean_model(cleaned_args[21]) if cleaned_args[21] else None |
| return cleaned_args |
|
|
| def process_wrapper(*args): |
| """Generator wrapper that forwards yields from save_settings_on_process.""" |
| for update in save_settings_on_process(*clean_inputs(*args)): |
| yield update |
|
|
| process_btn.click( |
| fn=process_wrapper, |
| inputs=[ |
| input_audio_file, model_dropdown, chunk_size, overlap, export_format, |
| optimize_mode, enable_amp, enable_tf32, enable_cudnn_benchmark, |
| use_tta, use_demud_phaseremix_inst, extract_instrumental, |
| use_apollo, apollo_chunk_size, apollo_overlap, |
| apollo_method, apollo_normal_model, apollo_midside_model, |
| use_matchering, matchering_passes, model_category, model_dropdown, |
| chunk_size_mode, chunk_size_custom |
| ], |
| outputs=[ |
| vocals_audio, instrumental_audio, phaseremix_audio, drum_audio, karaoke_audio, |
| other_audio, bass_audio, effects_audio, speech_audio, bleed_audio, music_audio, |
| dry_audio, male_audio, female_audio, |
| mid_audio, side_audio, |
| separation_process_status, separation_progress_html |
| ] |
| ) |
|
|
| auto_process_btn.click( |
| fn=save_auto_ensemble_settings, |
| inputs=[ |
| auto_input_audio_file, |
| selected_models, |
| auto_chunk_size, |
| auto_overlap, |
| export_format2, |
| auto_use_tta, |
| auto_extract_instrumental, |
| auto_ensemble_type, |
| gr.State(None), |
| auto_use_apollo, |
| auto_apollo_normal_model, |
| auto_apollo_chunk_size, |
| auto_apollo_overlap, |
| auto_apollo_method, |
| auto_use_matchering, |
| auto_matchering_passes, |
| auto_apollo_midside_model |
| ], |
| outputs=[auto_output_audio, ensemble_process_status, ensemble_progress_html] |
| ) |
|
|
| direct_download_btn.click( |
| fn=download_callback, |
| inputs=[direct_url_input, gr.State('direct'), cookie_file], |
| outputs=[direct_download_output, direct_download_status, input_audio_file, auto_input_audio_file, original_audio, original_audio2] |
| ) |
|
|
| refresh_output_btn.click( |
| fn=refresh_auto_output, |
| inputs=[], |
| outputs=[auto_output_audio, ensemble_process_status] |
| ) |
|
|
| refresh_btn.click(fn=update_file_list, outputs=file_dropdown) |
| ensemble_process_btn.click(fn=ensemble_audio_fn, inputs=[file_dropdown, ensemble_type, weights_input], outputs=[ensemble_output_audio, ensemble_status]) |
|
|
| return demo |
|
|
