ASesYusuf1/SESA_Audio_Separation

1import os2import json3from pathlib import Path4 5# Define config directory in Google Drive6CONFIG_DIR = "/content/drive/MyDrive/SESA-Config"7CONFIG_FILE = os.path.join(CONFIG_DIR, "config.json")8 9def load_config():10    """Load configuration from config.json."""11    default_config = {12        "favorites": [],13        "settings": {14            "chunk_size": 352800,15            "overlap": 2,16            "export_format": "wav FLOAT",17            "optimize_mode": "channels_last",18            "enable_amp": True,19            "enable_tf32": True,20            "enable_cudnn_benchmark": True,21            "auto_use_tta": False,22            "use_tta": False,23            "use_demud_phaseremix_inst": False,24            "auto_extract_instrumental": False,25            "extract_instrumental": False,26            "use_apollo": False,27            "auto_use_apollo": False,28            "auto_apollo_chunk_size": 19,29            "auto_apollo_overlap": 2,30            "auto_apollo_method": "normal_method",31            "auto_apollo_normal_model": "Apollo Universal Model",32            "auto_apollo_midside_model": "Apollo Universal Model",33            "apollo_chunk_size": 19,34            "apollo_overlap": 2,35            "apollo_method": "normal_method",36            "apollo_normal_model": "Apollo Universal Model",37            "apollo_midside_model": "Apollo Universal Model",38            "use_matchering": False,39            "auto_use_matchering": False,40            "matchering_passes": 1,41            "auto_matchering_passes": 1,42            "model_category": "Vocal Models",43            "selected_model": None,44            "auto_category": "Vocal Models",45            "selected_models": [],46            "auto_ensemble_type": "avg_wave",47            "manual_ensemble_type": "avg_wave",48            "auto_category_dropdown": "Vocal Models",49            "manual_weights": ""50        },51        "presets": {}52    }53 54    os.makedirs(CONFIG_DIR, exist_ok=True)55    if not os.path.exists(CONFIG_FILE):56        with open(CONFIG_FILE, "w", encoding="utf-8") as f:57            json.dump(default_config, f, indent=2)58        return default_config59 60    try:61        with open(CONFIG_FILE, "r", encoding="utf-8") as f:62            config = json.load(f)63        # Merge with default config to ensure all keys exist64        for key, value in default_config.items():65            if key not in config:66                config[key] = value67            elif isinstance(value, dict):68                for subkey, subvalue in value.items():69                    if subkey not in config[key]:70                        config[key][subkey] = subvalue71        return config72    except json.JSONDecodeError:73        print("Warning: config.json is corrupted. Creating a new one.")74        with open(CONFIG_FILE, "w", encoding="utf-8") as f:75            json.dump(default_config, f, indent=2)76        return default_config77 78def save_config(favorites, settings, presets):79    """Save configuration to config.json."""80    config = {81        "favorites": favorites,82        "settings": settings,83        "presets": presets84    }85    os.makedirs(CONFIG_DIR, exist_ok=True)86    with open(CONFIG_FILE, "w", encoding="utf-8") as f:87        json.dump(config, f, indent=2)88 89def update_favorites(favorites, model, add=True):90    """Update favorites list."""91    cleaned_model = model92    new_favorites = favorites.copy()93    if add and cleaned_model not in new_favorites:94        new_favorites.append(cleaned_model)95    elif not add and cleaned_model in new_favorites:96        new_favorites.remove(cleaned_model)97    return new_favorites98 99def save_preset(presets, preset_name, models, ensemble_method, **kwargs):100    """Save a preset."""101    new_presets = presets.copy()102    cleaned_models = [clean_model(model) for model in models]103    new_presets[preset_name] = {104        "models": cleaned_models,105        "ensemble_method": ensemble_method,106        "chunk_size": kwargs.get("chunk_size", load_config()["settings"]["chunk_size"]),107        "overlap": kwargs.get("overlap", load_config()["settings"]["overlap"]),108        "auto_use_tta": kwargs.get("auto_use_tta", load_config()["settings"]["auto_use_tta"]),109        "auto_extract_instrumental": kwargs.get("auto_extract_instrumental", load_config()["settings"]["auto_extract_instrumental"]),110        "use_apollo": kwargs.get("use_apollo", load_config()["settings"]["use_apollo"]),111        "auto_apollo_chunk_size": kwargs.get("auto_apollo_chunk_size", load_config()["settings"]["auto_apollo_chunk_size"]),112        "auto_category_dropdown": kwargs.get("auto_category_dropdown", load_config()["settings"]["auto_category_dropdown"]),  # Save category113        "auto_apollo_overlap": kwargs.get("auto_apollo_overlap", load_config()["settings"]["auto_apollo_overlap"]),114        "auto_apollo_method": kwargs.get("auto_apollo_method", load_config()["settings"]["auto_apollo_method"]),115        "auto_apollo_normal_model": kwargs.get("auto_apollo_normal_model", load_config()["settings"]["auto_apollo_normal_model"]),116        "auto_apollo_midside_model": kwargs.get("auto_apollo_midside_model", load_config()["settings"]["auto_apollo_midside_model"]),117        "auto_use_matchering": kwargs.get("use_matchering", load_config()["settings"]["use_matchering"]),118        "auto_matchering_passes": kwargs.get("matchering_passes", load_config()["settings"]["matchering_passes"]),119        "auto_category": kwargs.get("auto_category", load_config()["settings"]["auto_category"])120    }121    return new_presets122 123def delete_preset(presets, preset_name):124    """Delete a preset."""125    new_presets = presets.copy()126    if preset_name in new_presets:127        del new_presets[preset_name]128    return new_presets129 130def clean_model(model):131    """Remove ⭐ from model name if present."""132    return model.replace(" ⭐", "") if isinstance(model, str) else model133