WebUi2.py

import os

os.chdir('/content/Music-Source-Separation-Training')
import torch
import yaml
import gradio as gr
import subprocess
import threading
import random
import time
import shutil
import librosa
import soundfile as sf
import numpy as np
import requests
import json
import locale
import shutil
from datetime import datetime
import glob
import yt_dlp
import validators
from pytube import YouTube
from google.colab import auth
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload
import io
import math
import hashlib
import re
import gc
import psutil
import concurrent.futures
from tqdm import tqdm
from google.oauth2.credentials import Credentials
import tempfile
from urllib.parse import urlparse
from urllib.parse import quote
import gdown


os.makedirs('/content/Music-Source-Separation-Training/input', exist_ok=True)
os.makedirs('/content/Music-Source-Separation-Training/output', exist_ok=True)
os.makedirs('/content/drive/MyDrive/output', exist_ok=True)
os.makedirs('/content/drive/MyDrive/ensemble_folder', exist_ok=True)
os.makedirs('/content/Music-Source-Separation-Training/old_output', exist_ok=True)
os.makedirs('/content/Music-Source-Separation-Training/auto_ensemble_temp', exist_ok=True)
os.makedirs('/content/Music-Source-Separation-Training/wav_folder', exist_ok=True)
shutil.rmtree('/content/Music-Source-Separation-Training/ensemble', ignore_errors=True)
shutil.rmtree('/content/Music-Source-Separation-Training/auto_ensemble_temp', ignore_errors=True)


def clear_old_output():
    old_output_folder = os.path.join(BASE_PATH, 'old_output')
    try:
        if not os.path.exists(old_output_folder):
            return "❌ Old output folder does not exist"
        
        # Tüm dosya ve alt klasörleri sil
        shutil.rmtree(old_output_folder)
        os.makedirs(old_output_folder, exist_ok=True)
        
        return "✅ Old outputs successfully cleared!"
    
    except Exception as e:
        error_msg = f"🔥 Error: {str(e)}"
        print(error_msg)
        return error_msg
    
    print("All files in old_output have been deleted.")


def shorten_filename(filename, max_length=30):
    """
    Shortens a filename to a specified maximum length
    
    Args:
        filename (str): The filename to be shortened
        max_length (int): Maximum allowed length for the filename
    
    Returns:
        str: Shortened filename
    """
    base, ext = os.path.splitext(filename)
    if len(base) <= max_length:
        return filename
    
    # Take first 15 and last 10 characters
    shortened = base[:15] + "..." + base[-10:] + ext
    return shortened

def update_progress(progress=gr.Progress()):
    def track_progress(percent):
        progress
        (percent/100)
    return track_progress

# Özel karakterleri temizlemek için
def clean_filename(title):
    return re.sub(r'[^\w\-_\. ]', '', title).strip()

def download_callback(url, download_type='direct', cookie_file=None):
    try:
        # 1. TEMİZLİK VE KLASÖR HAZIRLIĞI
        BASE_PATH = "/content/Music-Source-Separation-Training"
        INPUT_DIR = os.path.join(BASE_PATH, "input")
        COOKIE_PATH = os.path.join(BASE_PATH, "cookies.txt")
        
        # Input klasörünü temizle ve yeniden oluştur
        clear_temp_folder(
            "/tmp",
            exclude_items=["gradio", "config.json"]
        )
        clear_directory(INPUT_DIR)
        os.makedirs(INPUT_DIR, exist_ok=True)

        # 2. URL DOĞRULAMA
        if not validators.url(url):
            return None, "❌ Invalid URL", None, None, None, None

        # 3. COOKIE YÖNETİMİ
        if cookie_file is not None:
            try:
                with open(cookie_file.name, "rb") as f:
                    cookie_content = f.read()
                with open(COOKIE_PATH, "wb") as f:
                    f.write(cookie_content)
                print("✅ Cookie file updated!")
            except Exception as e:
                print(f"⚠️ Cookie installation error: {str(e)}")

        wav_path = None
        download_success = False

        # 4. İNDİRME TÜRÜNE GÖRE İŞLEM
        if download_type == 'drive':
            # GOOGLE DRIVE İNDİRME
            try:
                file_id = re.search(r'/d/([^/]+)', url).group(1) if '/d/' in url else url.split('id=')[-1]
                original_filename = "drive_download.wav"
                
                # Gdown ile indirme
                output_path = os.path.join(INPUT_DIR, original_filename)
                gdown.download(
                    f'https://drive.google.com/uc?id={file_id}',
                    output_path,
                    quiet=True,
                    fuzzy=True
                )
                
                if os.path.exists(output_path) and os.path.getsize(output_path) > 0:
                    wav_path = output_path
                    download_success = True
                    print(f"✅ Downloaded from Google Drive: {wav_path}")
                else:
                    raise Exception("File size zero or file not created")

            except Exception as e:
                error_msg = f"❌ Google Drive download error: {str(e)}"
                print(error_msg)
                return None, error_msg, None, None, None, None

        else:
            # YOUTUBE/DİREKT LİNK İNDİRME
            ydl_opts = {
                'format': 'bestaudio/best',
                'outtmpl': os.path.join(INPUT_DIR, '%(title)s.%(ext)s'),
                'postprocessors': [{
                    'key': 'FFmpegExtractAudio',
                    'preferredcodec': 'wav',
                    'preferredquality': '0'
                }],
                'cookiefile': COOKIE_PATH if os.path.exists(COOKIE_PATH) else None,
                'nocheckcertificate': True,
                'ignoreerrors': True,
                'retries': 3
            }

            try:
                with yt_dlp.YoutubeDL(ydl_opts) as ydl:
                    info_dict = ydl.extract_info(url, download=True)
                    temp_path = ydl.prepare_filename(info_dict)
                    wav_path = os.path.splitext(temp_path)[0] + '.wav'
                    
                    if os.path.exists(wav_path):
                        download_success = True
                        print(f"✅ Downloaded successfully: {wav_path}")
                    else:
                        raise Exception("WAV conversion failed")

            except Exception as e:
                error_msg = f"❌ Download error: {str(e)}"
                print(error_msg)
                return None, error_msg, None, None, None, None

        # 5. SON KONTROLLER VE TEMİZLİK
        if download_success and wav_path:
            # Input klasöründeki gereksiz dosyaları temizle
            for f in os.listdir(INPUT_DIR):
                if f != os.path.basename(wav_path):
                    os.remove(os.path.join(INPUT_DIR, f))
            
            return (
                gr.File(value=wav_path),
                "🎉 Downloaded successfully!",
                gr.File(value=wav_path),
                gr.File(value=wav_path),
                gr.Audio(value=wav_path),
                gr.Audio(value=wav_path)
            )

        return None, "❌ Download failed", None, None, None, None

    except Exception as e:
        error_msg = f"🔥 Critical Error: {str(e)}"
        print(error_msg)
        return None, error_msg, None, None, None, None

        
# Hook function to track download progress
def download_progress_hook(d):
    if d['status'] == 'finished':
        print('Download complete, conversion in progress...')
    elif d['status'] == 'downloading':
        downloaded_bytes = d.get('downloaded_bytes', 0)
        total_bytes = d.get('total_bytes') or d.get('total_bytes_estimate', 0)
        if total_bytes > 0:
            percent = downloaded_bytes * 100. / total_bytes
            print(f'Downloading: {percent:.1f}%')

# Define the global variable at the top
INPUT_DIR = "/content/Music-Source-Separation-Training/input"

def download_file(url):
    # Encode the URL to handle spaces and special characters
    encoded_url = quote(url, safe=':/')

    path = 'ckpts'
    os.makedirs(path, exist_ok=True)
    filename = os.path.basename(encoded_url)
    file_path = os.path.join(path, filename)

    if os.path.exists(file_path):
        print(f"File '{filename}' already exists at '{path}'.")
        return

    try:
        response = torch.hub.download_url_to_file(encoded_url, file_path)
        print(f"File '{filename}' downloaded successfully")
    except Exception as e:
        print(f"Error downloading file '{filename}' from '{url}': {e}")
        


def generate_random_port():
    return random.randint(1000, 9000)

    clear_memory()

# Markdown annotations
markdown_intro = """
# Voice Parsing Tool

This tool is used to parse audio files.
"""

class IndentDumper(yaml.Dumper):
    def increase_indent(self, flow=False, indentless=False):
        return super(IndentDumper, self).increase_indent(flow, False)


def tuple_constructor(loader, node):
    # Load the sequence of values from the YAML node
    values = loader.construct_sequence(node)
    # Return a tuple constructed from the sequence
    return tuple(values)

# Register the constructor with PyYAML
yaml.SafeLoader.add_constructor('tag:yaml.org,2002:python/tuple',
tuple_constructor)



def conf_edit(config_path, chunk_size, overlap):
    with open(config_path, 'r') as f:
        data = yaml.load(f, Loader=yaml.SafeLoader)

    # handle cases where 'use_amp' is missing from config:
    if 'use_amp' not in data.keys():
      data['training']['use_amp'] = True

    data['audio']['chunk_size'] = chunk_size
    data['inference']['num_overlap'] = overlap

    if data['inference']['batch_size'] == 1:
      data['inference']['batch_size'] = 2

    print("Using custom overlap and chunk_size values:")
    print(f"overlap = {data['inference']['num_overlap']}")
    print(f"chunk_size = {data['audio']['chunk_size']}")
    print(f"batch_size = {data['inference']['batch_size']}")


    with open(config_path, 'w') as f:
        yaml.dump(data, f, default_flow_style=False, sort_keys=False, Dumper=IndentDumper, allow_unicode=True)
        
def save_uploaded_file(uploaded_file, is_input=False, target_dir=None):
    try:
        # Medya dosya uzantıları
        media_extensions = ['.mp3', '.wav', '.flac', '.aac', '.ogg', '.m4a', '.mp4']
        
        # Hedef dizini belirle
        if target_dir is None:
            target_dir = INPUT_DIR if is_input else VİDEO_TEMP
        
        # Zaman damgası pattern'leri
        timestamp_patterns = [
            r'_\d{8}_\d{6}_\d{6}$',  # _20231215_123456_123456
            r'_\d{14}$',             # _20231215123456
            r'_\d{10}$',             # _1702658400
            r'_\d+$'                 # Herhangi bir sayı
        ]
        
        # Dosya adını al
        if hasattr(uploaded_file, 'name'):
            original_filename = os.path.basename(uploaded_file.name)
        else:
            original_filename = os.path.basename(str(uploaded_file))
        
        # Dosya adını temizle (sadece input'lar için)
        if is_input:
            base_filename = original_filename
            # Zaman damgalarını sil
            for pattern in timestamp_patterns:
                base_filename = re.sub(pattern, '', base_filename)
            # Çoklu uzantıları sil
            for ext in media_extensions:
                base_filename = base_filename.replace(ext, '')
            
            # Dosya uzantısını belirle
            file_ext = next(
                (ext for ext in media_extensions if original_filename.lower().endswith(ext)),
                '.wav'
            )
            clean_filename = f"{base_filename.strip('_- ')}{file_ext}"
        else:
            clean_filename = original_filename

        # Hedef dizini belirle (DÜZELTME BURADA)
        target_directory = INPUT_DIR if is_input else OUTPUT_DIR
        target_path = os.path.join(target_dir, clean_filename)
        
        # Dizini oluştur (yoksa)
        os.makedirs(target_directory, exist_ok=True)
        
        # Dizindeki TÜM önceki dosyaları sil
        for filename in os.listdir(target_directory):
            file_path = os.path.join(target_directory, filename)
            try:
                if os.path.isfile(file_path) or os.path.islink(file_path):
                    os.unlink(file_path)
                elif os.path.isdir(file_path):
                    shutil.rmtree(file_path)
            except Exception as e:
                print(f"{file_path} Not deleted: {e}")

        # Yeni dosyayı kaydet
        if hasattr(uploaded_file, 'read'):
            with open(target_path, "wb") as f:
                f.write(uploaded_file.read())
        else:
            shutil.copy(uploaded_file, target_path)
            
        print(f"File saved successfully: {os.path.basename(target_path)}")
        return target_path
    
    except Exception as e:
        print(f"File save error: {e}")
        return None

        clear_memory()

def save_uploaded_file(uploaded_file, is_input=False, target_dir=None):
    try:
        # Medya dosya uzantıları
        media_extensions = ['.mp3', '.wav', '.flac', '.aac', '.ogg', '.m4a', '.mp4']
        
        # Hedef dizini belirle
        if target_dir is None:
            target_dir = INPUT_DIR if is_input else OUTPUT_DIR
        
        # Zaman damgası pattern'leri
        timestamp_patterns = [
            r'_\d{8}_\d{6}_\d{6}$',  # _20231215_123456_123456
            r'_\d{14}$',             # _20231215123456
            r'_\d{10}$',             # _1702658400
            r'_\d+$'                 # Herhangi bir sayı
        ]
        
        # Dosya adını al
        if hasattr(uploaded_file, 'name'):
            original_filename = os.path.basename(uploaded_file.name)
        else:
            original_filename = os.path.basename(str(uploaded_file))
        
        # Dosya adını temizle (sadece input'lar için)
        if is_input:
            base_filename = original_filename
            # Zaman damgalarını sil
            for pattern in timestamp_patterns:
                base_filename = re.sub(pattern, '', base_filename)
            # Çoklu uzantıları sil
            for ext in media_extensions:
                base_filename = base_filename.replace(ext, '')
            
            # Dosya uzantısını belirle
            file_ext = next(
                (ext for ext in media_extensions if original_filename.lower().endswith(ext)),
                '.wav'
            )
            clean_filename = f"{base_filename.strip('_- ')}{file_ext}"
        else:
            clean_filename = original_filename

        # Hedef dizini belirle
        target_directory = INPUT_DIR if is_input else OUTPUT_DIR
        target_path = os.path.join(target_directory, clean_filename)
        
        # Dizini oluştur (yoksa)
        os.makedirs(target_directory, exist_ok=True)
        
        # Eğer dosya zaten varsa, sil
        if os.path.exists(target_path):
            os.remove(target_path)

        # Yeni dosyayı kaydet
        if hasattr(uploaded_file, 'read'):
            with open(target_path, "wb") as f:
                f.write(uploaded_file.read())
        else:
            shutil.copy(uploaded_file, target_path)
            
        print(f"File saved successfully: {os.path.basename(target_path)}")
        return target_path
    
    except Exception as e:
        print(f"File save error: {e}")
        return None


def clear_temp_folder(folder_path, exclude_items=None):
    """
    Safely clears contents of a directory while preserving specified items
    
    Args:
        folder_path (str): Path to directory to clean
        exclude_items (list): Items to preserve (e.g., ['gradio', 'important.log'])
        
    Returns:
        bool: True if successful, False if failed
    """
    try:
        # Validate directory existence
        if not os.path.exists(folder_path):
            print(f"⚠️ Directory does not exist: {folder_path}")
            return False
            
        if not os.path.isdir(folder_path):
            print(f"⚠️ Path is not a directory: {folder_path}")
            return False

        # Initialize exclusion list
        exclude_items = exclude_items or []
        preserved_items = []

        # Process directory contents
        for item_name in os.listdir(folder_path):
            item_path = os.path.join(folder_path, item_name)
            
            # Skip excluded items
            if item_name in exclude_items:
                preserved_items.append(item_path)
                continue
                
            try:
                # Delete files and symlinks
                if os.path.isfile(item_path) or os.path.islink(item_path):
                    os.unlink(item_path)
                    print(f"🗑️ File removed: {item_path}")
                    
                # Delete directories
                elif os.path.isdir(item_path):
                    shutil.rmtree(item_path)
                    print(f"🗂️ Directory removed: {item_path}")
                    
            except PermissionError as pe:
                print(f"🔒 Permission denied: {item_path} ({str(pe)})")
                continue
                
            except Exception as e:
                print(f"⚠️ Error deleting {item_path}: {str(e)}")
                continue

        # Print summary
        print(f"\n✅ Cleaning completed: {folder_path}")
        print(f"Total preserved items: {len(preserved_items)}")
        if preserved_items:
            print("Preserved items:")
            for item in preserved_items:
                print(f"  - {item}")
                
        return True

    except Exception as e:
        print(f"❌ Critical error: {str(e)}")
        return False


def handle_file_upload(file_obj, file_path_input, is_auto_ensemble=False):
    try:
        BASE_PATH = "/content/Music-Source-Separation-Training"
        INPUT_DIR = os.path.join(BASE_PATH, "input")
        
        # Yeni: Önceki dosyaları kontrol et
        existing_files = os.listdir(INPUT_DIR)
        new_file = None

        # Dosya yolu girilmişse
        if file_path_input and os.path.exists(file_path_input):
            new_file = file_path_input
        # Dosya yüklenmişse
        elif file_obj:
            new_file = file_obj.name  # Gradio'nun geçici dosya yolu

        # Yeni dosya yoksa mevcut dosyayı koru
        if not new_file and existing_files:
            kept_file = os.path.join(INPUT_DIR, existing_files[0])
            return [
                gr.File(value=kept_file),
                gr.Audio(value=kept_file)
            ]

        # Yeni dosya varsa temizle ve yükle
        if new_file:
            clear_directory(INPUT_DIR)  # Sadece yeni dosya geldiğinde temizle
            saved_path = save_uploaded_file(new_file, is_input=True)
            return [
                gr.File(value=saved_path),
                gr.Audio(value=saved_path)
            ]

        return [None, None]
    
    except Exception as e:
        print(f"Error: {str(e)}")
        return [None, None]
        
def move_old_files(output_folder):
    old_output_folder = os.path.join(BASE_PATH, 'old_output')
    os.makedirs(old_output_folder, exist_ok=True)

    # Eski dosyaları taşı ve adlarının sonuna "old" ekle
    for filename in os.listdir(output_folder):
        file_path = os.path.join(output_folder, filename)
        if os.path.isfile(file_path):
            # Yeni dosya adını oluştur
            new_filename = f"{os.path.splitext(filename)[0]}_old{os.path.splitext(filename)[1]}"
            new_file_path = os.path.join(old_output_folder, new_filename)
            shutil.move(file_path, new_file_path) 

def move_wav_files2(INPUT_DIR):
    ENSEMBLE_DIR = os.path.join(BASE_PATH, 'ensemble')
    os.makedirs(ENSEMBLE_DIR, exist_ok=True)

    # Eski dosyaları taşı ve adlarının sonuna "old" ekle
    for filename in os.listdir(INPUT_DIR):
        file_path = os.path.join(INPUT_DIR, filename)
        if os.path.isfile(file_path):
            # Yeni dosya adını oluştur
            new_filename = f"{os.path.splitext(filename)[0]}_ensemble{os.path.splitext(filename)[1]}"
            new_file_path = os.path.join(ENSEMBLE_DIR, new_filename)
            shutil.move(file_path, new_file_path)             


def extract_model_name(full_model_string):
    """
    Function to clear model name
    """
    if not full_model_string:
        return ""

    cleaned = str(full_model_string)

    # Remove the description
    if ' - ' in cleaned:
        cleaned = cleaned.split(' - ')[0]

    # Remove emoji prefixes
    emoji_prefixes = ['✅ ', '👥 ', '🗣️ ', '🏛️ ', '🔇 ', '🔉 ', '🎬 ', '🎼 ', '✅(?) ']
    for prefix in emoji_prefixes:
        if cleaned.startswith(prefix):
            cleaned = cleaned[len(prefix):]

    return cleaned.strip()

COOKIE_PATH = '/content/'
BASE_PATH = '/content/Music-Source-Separation-Training'
INPUT_DIR = os.path.join(BASE_PATH, 'input')
AUTO_ENSEMBLE_TEMP = os.path.join(BASE_PATH, 'auto_ensemble_temp')
OUTPUT_DIR = '/content/drive/MyDrive/output'
OLD_OUTPUT_DIR = '/content/drive/MyDrive/old_output'
AUTO_ENSEMBLE_OUTPUT = '/content/drive/MyDrive/ensemble_folder'
INFERENCE_SCRIPT_PATH = '/content/Music-Source-Separation-Training/inference.py'
VİDEO_TEMP = '/content/Music-Source-Separation-Training/video_temp'
ENSEMBLE_DIR = '/content/Music-Source-Separation-Training/ensemble'
os.makedirs(VİDEO_TEMP, exist_ok=True)  # Klasörü oluşturduğundan emin ol
Backup = '/content/backup'

def clear_directory(directory):
    """Deletes all files in the given directory."""
    files = glob.glob(os.path.join(directory, '*'))  # Dizin içindeki tüm dosyaları al
    for f in files:
        try:
            os.remove(f)  # remove files
        except Exception as e:
            print(f"{f} could not be deleted: {e}")     

def create_directory(directory):
    """Creates the given directory (if it exists, if not)."""
    if not os.path.exists(directory):
        os.makedirs(directory)
        print(f"{directory} directory created.")
    else:
        print(f"{directory} directory already exists.")     

def convert_to_wav(file_path):
    """Converts the audio file to WAV format and moves it to the ensemble directory."""
    
    BASE_DIR = "/content/Music-Source-Separation-Training"
    ENSEMBLE_DIR = os.path.join(BASE_DIR, "ensemble")  # Define the ensemble directory
    os.makedirs(ENSEMBLE_DIR, exist_ok=True)  # Create the ensemble directory if it doesn't exist

    original_filename = os.path.basename(file_path)
    filename, ext = os.path.splitext(original_filename)
    
    # If already a WAV file, return its path directly
    if ext.lower() == '.wav':
        return file_path  # Return the original path if it's already a WAV file

    try:
        # Prepare for WAV conversion
        wav_output = os.path.join(ENSEMBLE_DIR, f"{filename}.wav")  # Save to ensemble directory
        
        # Run FFmpeg command to convert to WAV
        command = [
            'ffmpeg', '-y', '-i', file_path,
            '-acodec', 'pcm_s16le', '-ar', '44100', wav_output
        ]
        subprocess.run(command, check=True, capture_output=True)

        return wav_output  # Return the path of the converted WAV file
        
    except subprocess.CalledProcessError as e:
        error_msg = f"FFmpeg Error ({e.returncode}): {e.stderr.decode()}"
        print(error_msg)
        return None
    except Exception as e:
        print(f"Error during conversion: {str(e)}")
        return None

def send_audio_file(file_path):
    try:
        if not os.path.exists(file_path):
            print(f"File not found: {file_path}")
            return None, "File not found"

        with open(file_path, 'rb') as f:
            data = f.read()
            print(f"Sending file: {file_path}, Size: {len(data)} bytes")
            return data, "Success"
    except Exception as e:
        print(f"Error sending file: {e}")
        return None, str(e)   
       

def process_audio(input_audio_file, model, chunk_size, overlap, export_format, use_tta, demud_phaseremix_inst, extract_instrumental, clean_model, *args, **kwargs): 
    # Determine the audio path
    if input_audio_file is not None:
        audio_path = input_audio_file.name
    else:
        # Check for existing files in INPUT_DIR
        create_directory(INPUT_DIR)  # Ensure the directory exists
        existing_files = os.listdir(INPUT_DIR)
        if existing_files:
            # Use the first existing file
            audio_path = os.path.join(INPUT_DIR, existing_files[0])
        else:
            print("No audio file provided and no existing file in input directory.")
            return [None] * 14  # Error case

    # Create necessary directories
    create_directory(OUTPUT_DIR)
    create_directory(OLD_OUTPUT_DIR)

    # Move old files to the OLD_OUTPUT_DIR
    move_old_files(OUTPUT_DIR)

    # Clean model name
    clean_model = extract_model_name(model)
    print(f"Processing audio from: {audio_path} using model: {clean_model}")

    # Model configuration (remaining code)
    model_type, config_path, start_check_point = "", "", ""

    if clean_model == 'VOCALS-InstVocHQ':
            model_type = 'mdx23c'
            config_path = 'ckpts/config_vocals_mdx23c.yaml'
            start_check_point = 'ckpts/model_vocals_mdx23c_sdr_10.17.ckpt'
            download_file('https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/config_vocals_mdx23c.yaml')
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.0/model_vocals_mdx23c_sdr_10.17.ckpt')

    elif clean_model == 'VOCALS-MelBand-Roformer (by KimberleyJSN)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_vocals_mel_band_roformer_kj.yaml'
            start_check_point = 'ckpts/MelBandRoformer.ckpt'
            download_file('https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/KimberleyJensen/config_vocals_mel_band_roformer_kj.yaml')
            download_file('https://huggingface.co/KimberleyJSN/melbandroformer/resolve/main/MelBandRoformer.ckpt')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'VOCALS-BS-Roformer_1297 (by viperx)':
            model_type = 'bs_roformer'
            config_path = 'ckpts/model_bs_roformer_ep_317_sdr_12.9755.yaml'
            start_check_point = 'ckpts/model_bs_roformer_ep_317_sdr_12.9755.ckpt'
            download_file('https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/viperx/model_bs_roformer_ep_317_sdr_12.9755.yaml')
            download_file('https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/model_bs_roformer_ep_317_sdr_12.9755.ckpt')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'VOCALS-BS-Roformer_1296 (by viperx)':
            model_type = 'bs_roformer'
            config_path = 'ckpts/model_bs_roformer_ep_368_sdr_12.9628.yaml'
            start_check_point = 'ckpts/model_bs_roformer_ep_368_sdr_12.9628.ckpt'
            download_file('https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/model_bs_roformer_ep_368_sdr_12.9628.ckpt')
            download_file('https://raw.githubusercontent.com/TRvlvr/application_data/main/mdx_model_data/mdx_c_configs/model_bs_roformer_ep_368_sdr_12.9628.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'VOCALS-BS-RoformerLargev1 (by unwa)':
            model_type = 'bs_roformer'
            config_path = 'ckpts/config_bsrofoL.yaml'
            start_check_point = 'ckpts/BS-Roformer_LargeV1.ckpt'
            download_file('https://huggingface.co/jarredou/unwa_bs_roformer/resolve/main/BS-Roformer_LargeV1.ckpt')
            download_file('https://huggingface.co/jarredou/unwa_bs_roformer/raw/main/config_bsrofoL.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'VOCALS-Mel-Roformer big beta 4 (by unwa)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_melbandroformer_big_beta4.yaml'
            start_check_point = 'ckpts/melband_roformer_big_beta4.ckpt'
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-big/resolve/main/melband_roformer_big_beta4.ckpt')
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-big/raw/main/config_melbandroformer_big_beta4.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'VOCALS-Melband-Roformer BigBeta5e (by unwa)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/big_beta5e.yaml'
            start_check_point = 'ckpts/big_beta5e.ckpt'
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-big/resolve/main/big_beta5e.ckpt')
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-big/resolve/main/big_beta5e.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'INST-Mel-Roformer v1 (by unwa)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_melbandroformer_inst.yaml'
            start_check_point = 'ckpts/melband_roformer_inst_v1.ckpt'
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/melband_roformer_inst_v1.ckpt')
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/raw/main/config_melbandroformer_inst.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'INST-Mel-Roformer v2 (by unwa)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_melbandroformer_inst_v2.yaml'
            start_check_point = 'ckpts/melband_roformer_inst_v2.ckpt'
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/melband_roformer_inst_v2.ckpt')
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/raw/main/config_melbandroformer_inst_v2.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'INST-VOC-Mel-Roformer a.k.a. duality (by unwa)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_melbandroformer_instvoc_duality.yaml'
            start_check_point = 'ckpts/melband_roformer_instvoc_duality_v1.ckpt'
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/resolve/main/melband_roformer_instvoc_duality_v1.ckpt')
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/raw/main/config_melbandroformer_instvoc_duality.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'INST-VOC-Mel-Roformer a.k.a. duality v2 (by unwa)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_melbandroformer_instvoc_duality.yaml'
            start_check_point = 'ckpts/melband_roformer_instvox_duality_v2.ckpt'
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/resolve/main/melband_roformer_instvox_duality_v2.ckpt')
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/raw/main/config_melbandroformer_instvoc_duality.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'KARAOKE-MelBand-Roformer (by aufr33 & viperx)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_mel_band_roformer_karaoke.yaml'
            start_check_point = 'ckpts/mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt'
            download_file('https://huggingface.co/jarredou/aufr33-viperx-karaoke-melroformer-model/resolve/main/mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt')
            download_file('https://huggingface.co/jarredou/aufr33-viperx-karaoke-melroformer-model/resolve/main/config_mel_band_roformer_karaoke.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'OTHER-BS-Roformer_1053 (by viperx)':
            model_type = 'bs_roformer'
            config_path = 'ckpts/model_bs_roformer_ep_937_sdr_10.5309.yaml'
            start_check_point = 'ckpts/model_bs_roformer_ep_937_sdr_10.5309.ckpt'
            download_file('https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/model_bs_roformer_ep_937_sdr_10.5309.ckpt')
            download_file('https://raw.githubusercontent.com/TRvlvr/application_data/main/mdx_model_data/mdx_c_configs/model_bs_roformer_ep_937_sdr_10.5309.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'CROWD-REMOVAL-MelBand-Roformer (by aufr33)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/model_mel_band_roformer _crowd.yaml'
            start_check_point = 'ckpts/mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt'
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.4/mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt')
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.4/model_mel_band_roformer_crowd.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'VOCALS-VitLarge23 (by ZFTurbo)':
            model_type = 'segm_models'
            config_path = 'ckpts/config_vocals_segm_models.yaml'
            start_check_point = 'ckpts/model_vocals_segm_models_sdr_9.77.ckpt'
            download_file('https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/refs/heads/main/configs/config_vocals_segm_models.yaml')
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.0/model_vocals_segm_models_sdr_9.77.ckpt')

    elif clean_model == 'CINEMATIC-BandIt_Plus (by kwatcharasupat)':
            model_type = 'bandit'
            config_path = 'ckpts/config_dnr_bandit_bsrnn_multi_mus64.yaml'
            start_check_point = 'ckpts/model_bandit_plus_dnr_sdr_11.47.chpt'
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.3/config_dnr_bandit_bsrnn_multi_mus64.yaml')
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.3/model_bandit_plus_dnr_sdr_11.47.chpt')

    elif clean_model == 'DRUMSEP-MDX23C_DrumSep_6stem (by aufr33 & jarredou)':
            model_type = 'mdx23c'
            config_path = 'ckpts/aufr33-jarredou_DrumSep_model_mdx23c_ep_141_sdr_10.8059.yaml'
            start_check_point = 'ckpts/aufr33-jarredou_DrumSep_model_mdx23c_ep_141_sdr_10.8059.ckpt'
            download_file('https://github.com/jarredou/models/releases/download/aufr33-jarredou_MDX23C_DrumSep_model_v0.1/aufr33-jarredou_DrumSep_model_mdx23c_ep_141_sdr_10.8059.ckpt')
            download_file('https://github.com/jarredou/models/releases/download/aufr33-jarredou_MDX23C_DrumSep_model_v0.1/aufr33-jarredou_DrumSep_model_mdx23c_ep_141_sdr_10.8059.yaml')

    elif clean_model == '4STEMS-SCNet_MUSDB18 (by starrytong)':
            model_type = 'scnet'
            config_path = 'ckpts/config_musdb18_scnet.yaml'
            start_check_point = 'ckpts/scnet_checkpoint_musdb18.ckpt'
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.6/config_musdb18_scnet.yaml')
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.6/scnet_checkpoint_musdb18.ckpt')

    elif clean_model == 'DE-REVERB-MDX23C (by aufr33 & jarredou)':
            model_type = 'mdx23c'
            config_path = 'ckpts/config_dereverb_mdx23c.yaml'
            start_check_point = 'ckpts/dereverb_mdx23c_sdr_6.9096.ckpt'
            download_file('https://huggingface.co/jarredou/aufr33_jarredou_MDXv3_DeReverb/resolve/main/dereverb_mdx23c_sdr_6.9096.ckpt')
            download_file('https://huggingface.co/jarredou/aufr33_jarredou_MDXv3_DeReverb/resolve/main/config_dereverb_mdx23c.yaml')

    elif clean_model == 'DENOISE-MelBand-Roformer-1 (by aufr33)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/model_mel_band_roformer_denoise.yaml'
            start_check_point = 'ckpts/denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt'
            download_file('https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt')
            download_file('https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/model_mel_band_roformer_denoise.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'DENOISE-MelBand-Roformer-2 (by aufr33)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/model_mel_band_roformer_denoise.yaml'
            start_check_point = 'ckpts/denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt'
            download_file('https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt')
            download_file('https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/model_mel_band_roformer_denoise.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'VOCALS-MelBand-Roformer Kim FT (by Unwa)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_kimmel_unwa_ft.yaml'
            start_check_point = 'ckpts/kimmel_unwa_ft.ckpt'
            download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/kimmel_unwa_ft.ckpt')
            download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/config_kimmel_unwa_ft.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'inst_v1e (by unwa)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_melbandroformer_inst.yaml'
            start_check_point = 'ckpts/inst_v1e.ckpt'
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/inst_v1e.ckpt')
            download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/config_melbandroformer_inst.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'bleed_suppressor_v1 (by unwa)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_bleed_suppressor_v1.yaml'
            start_check_point = 'ckpts/bleed_suppressor_v1.ckpt'
            download_file('https://huggingface.co/ASesYusuf1/MODELS/resolve/main/bleed_suppressor_v1.ckpt')
            download_file('https://huggingface.co/ASesYusuf1/MODELS/resolve/main/config_bleed_suppressor_v1.yaml')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'VOCALS-MelBand-Roformer (by Becruily)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_instrumental_becruily.yaml'
            start_check_point = 'ckpts/mel_band_roformer_vocals_becruily.ckpt'
            download_file('https://huggingface.co/becruily/mel-band-roformer-vocals/resolve/main/config_vocals_becruily.yaml')
            download_file('https://huggingface.co/becruily/mel-band-roformer-vocals/resolve/main/mel_band_roformer_vocals_becruily.ckpt')
            conf_edit(config_path, chunk_size, overlap)
    
    elif clean_model == 'INST-MelBand-Roformer (by Becruily)':
            model_type = 'mel_band_roformer'
            config_path = 'ckpts/config_instrumental_becruily.yaml'
            start_check_point = 'ckpts/mel_band_roformer_instrumental_becruily.ckpt'
            download_file('https://huggingface.co/becruily/mel-band-roformer-instrumental/resolve/main/config_instrumental_becruily.yaml')
            download_file('https://huggingface.co/becruily/mel-band-roformer-instrumental/resolve/main/mel_band_roformer_instrumental_becruily.ckpt')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == '4STEMS-SCNet_XL_MUSDB18 (by ZFTurbo)':
            model_type = 'scnet'
            config_path = 'ckpts/config_musdb18_scnet_xl.yaml'
            start_check_point = 'ckpts/model_scnet_ep_54_sdr_9.8051.ckpt'
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.13/config_musdb18_scnet_xl.yaml')
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.13/model_scnet_ep_54_sdr_9.8051.ckpt')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == '4STEMS-SCNet_Large (by starrytong)':
            model_type = 'scnet'
            config_path = 'ckpts/config_musdb18_scnet_large_starrytong.yaml'
            start_check_point = 'ckpts/SCNet-large_starrytong_fixed.ckpt'
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.9/config_musdb18_scnet_large_starrytong.yaml')
            download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.9/SCNet-large_starrytong_fixed.ckpt')
            conf_edit(config_path, chunk_size, overlap)

    elif clean_model == '4STEMS-BS-Roformer_MUSDB18 (by ZFTurbo)':
           model_type = 'bs_roformer'
           config_path = 'ckpts/config_bs_roformer_384_8_2_485100.yaml'
           start_check_point = 'ckpts/model_bs_roformer_ep_17_sdr_9.6568.ckpt'
           download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.12/config_bs_roformer_384_8_2_485100.yaml')
           download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.12/model_bs_roformer_ep_17_sdr_9.6568.ckpt')
           conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'DE-REVERB-MelBand-Roformer aggr./v2/19.1729 (by anvuew)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/dereverb_mel_band_roformer_anvuew.yaml'
          start_check_point = 'ckpts/dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt'
          download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt')
          download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml')
          conf_edit(config_path, chunk_size, overlap)
          
    elif clean_model == 'DE-REVERB-Echo-MelBand-Roformer (by Sucial)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/config_dereverb-echo_mel_band_roformer.yaml'
          start_check_point = 'ckpts/dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt'
          download_file('https://huggingface.co/Sucial/Dereverb-Echo_Mel_Band_Roformer/resolve/main/dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt')
          download_file('https://huggingface.co/Sucial/Dereverb-Echo_Mel_Band_Roformer/resolve/main/config_dereverb-echo_mel_band_roformer.yaml')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'dereverb_mel_band_roformer_less_aggressive_anvuew':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/dereverb_mel_band_roformer_anvuew.yaml'
          start_check_point = 'ckpts/dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt'
          download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml')
          download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'dereverb_mel_band_roformer_anvuew':
          model_type = 'mel_band_roformer'
          config_path = 'dereverb_mel_band_roformer_anvuew.yaml'
          start_check_point = 'ckpts/dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt'
          download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml')
          download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt')
          conf_edit(config_path, chunk_size, overlap)  


    elif clean_model == 'inst_gabox (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/inst_gabox.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'inst_gaboxBV1 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/inst_gaboxBv1.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxBv1.ckpt')
          conf_edit(config_path, chunk_size, overlap)


    elif clean_model == 'inst_gaboxBV2 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/inst_gaboxBv2.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxBv2.ckpt')
          conf_edit(config_path, chunk_size, overlap)     


    elif clean_model == 'inst_gaboxBFV1 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/gaboxFv1.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv1.ckpt')
          conf_edit(config_path, chunk_size, overlap)


    elif clean_model == 'inst_gaboxFV2 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/inst_gaboxFv2.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv2.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    
    elif clean_model == 'VOCALS-Male Female-BS-RoFormer Male Female Beta 7_2889 (by aufr33)':
          model_type = 'bs_roformer'
          config_path = 'ckpts/config_chorus_male_female_bs_roformer.yaml'
          start_check_point = 'ckpts/bs_roformer_male_female_by_aufr33_sdr_7.2889.ckpt'
          download_file('https://huggingface.co/RareSirMix/AIModelRehosting/resolve/main/bs_roformer_male_female_by_aufr33_sdr_7.2889.ckpt')
          download_file('https://huggingface.co/Sucial/Chorus_Male_Female_BS_Roformer/resolve/main/config_chorus_male_female_bs_roformer.yaml')
          conf_edit(config_path, chunk_size, overlap)


    elif clean_model == 'VOCALS-MelBand-Roformer Kim FT 2 (by Unwa)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/config_kimmel_unwa_ft.yaml'
          start_check_point = 'ckpts/kimmel_unwa_ft2.ckpt'
          download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/config_kimmel_unwa_ft.yaml')
          download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/kimmel_unwa_ft2.ckpt')
          conf_edit(config_path, chunk_size, overlap)      

    elif clean_model == 'voc_gaboxBSroformer (by Gabox)':
          model_type = 'bs_roformer'
          config_path = 'ckpts/voc_gaboxBSroformer.yaml'
          start_check_point = 'ckpts/voc_gaboxBSR.ckpt'
          download_file('https://huggingface.co/GaboxR67/BSRoformerVocTest/resolve/main/voc_gaboxBSroformer.yaml')
          download_file('https://huggingface.co/GaboxR67/BSRoformerVocTest/resolve/main/voc_gaboxBSR.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'voc_gaboxMelReformer (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/voc_gabox.yaml'
          start_check_point = 'ckpts/voc_gabox.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'voc_gaboxMelReformerFV1 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/voc_gabox.yaml'
          start_check_point = 'ckpts/voc_gaboxFv1.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gaboxFv1.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'voc_gaboxMelReformerFV2 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/voc_gabox.yaml'
          start_check_point = 'ckpts/voc_gaboxFv2.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gaboxFv2.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'inst_GaboxFv3 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/inst_gaboxFv3.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv3.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'Intrumental_Gabox (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/intrumental_gabox.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/intrumental_gabox.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'inst_Fv4Noise (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/inst_Fv4Noise.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_Fv4Noise.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'inst_V5 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/INSTV5.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV5.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'SYH99999/MelBandRoformerSYHFTB1_Model1 (by Amane)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/config.yaml'
          start_check_point = 'ckpts/model.ckpt'
          download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/config.yaml')
          download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/model.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'SYH99999/MelBandRoformerSYHFTB1_Model2 (by Amane)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/config.yaml'
          start_check_point = 'ckpts/model2.ckpt'
          download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/config.yaml')
          download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/model2.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'SYH99999/MelBandRoformerSYHFTB1_Model3 (by Amane)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/config.yaml'
          start_check_point = 'ckpts/model3.ckpt'
          download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/config.yaml')
          download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/model3.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'VOCALS-MelBand-Roformer Kim FT 2 Blendless (by unwa)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/config_kimmel_unwa_ft.yaml'
          start_check_point = 'ckpts/kimmel_unwa_ft2_bleedless.ckpt'
          download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/config_kimmel_unwa_ft.yaml')
          download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/kimmel_unwa_ft2_bleedless.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'inst_gaboxFV1 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/inst_gaboxFv1.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv1.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'inst_gaboxFV6 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/INSTV6.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV6.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'denoisedebleed (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/model_mel_band_roformer_denoise.yaml'
          start_check_point = 'ckpts/denoisedebleed.ckpt'
          download_file('https://huggingface.co/poiqazwsx/melband-roformer-denoise/resolve/main/model_mel_band_roformer_denoise.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/denoisedebleed.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'INSTV5N (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/INSTV5N.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV5N.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'Voc_Fv3 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/voc_gabox.yaml'
          start_check_point = 'ckpts/voc_Fv3.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_Fv3.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'MelBandRoformer4StemFTLarge (SYH99999)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/config.yaml'
          start_check_point = 'ckpts/MelBandRoformer4StemFTLarge.ckpt'
          download_file('https://huggingface.co/SYH99999/MelBandRoformer4StemFTLarge/resolve/main/config.yaml')
          download_file('https://huggingface.co/SYH99999/MelBandRoformer4StemFTLarge/resolve/main/MelBandRoformer4StemFTLarge.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'dereverb_mel_band_roformer_mono (by anvuew)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/dereverb_mel_band_roformer_anvuew.yaml'
          start_check_point = 'ckpts/dereverb_mel_band_roformer_mono_anvuew_sdr_20.4029.ckpt'
          download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml')
          download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_mono_anvuew_sdr_20.4029.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'INSTV6N (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/INSTV6N.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV6N.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'KaraokeGabox':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/config_mel_band_roformer_karaoke.yaml'
          start_check_point = 'ckpts/KaraokeGabox.ckpt'
          download_file('https://github.com/deton24/Colab-for-new-MDX_UVR_models/releases/download/v1.0.0/config_mel_band_roformer_karaoke.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/blob/main/melbandroformers/experimental/KaraokeGabox.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'FullnessVocalModel (by Amane)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/config.yaml'
          start_check_point = 'ckpts/FullnessVocalModel.ckpt'
          download_file('https://huggingface.co/Aname-Tommy/MelBandRoformers/blob/main/config.yaml')
          download_file('https://huggingface.co/Aname-Tommy/MelBandRoformers/blob/main/FullnessVocalModel.ckpt')
          conf_edit(config_path, chunk_size, overlap)

    elif clean_model == 'Inst_GaboxV7 (by Gabox)':
          model_type = 'mel_band_roformer'
          config_path = 'ckpts/inst_gabox.yaml'
          start_check_point = 'ckpts/Inst_GaboxV7.ckpt'
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
          download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/Inst_GaboxV7.ckpt')
          conf_edit(config_path, chunk_size, overlap)      









    # Other model options will be added here...
    # (All the elif blocks you gave in the previous code will go here)


    else:
        print(f"Unsupported model: {clean_model}")
        return [None] * 14  # Hata durumu

    result = run_command_and_process_files(model_type, config_path, start_check_point, INPUT_DIR, OUTPUT_DIR, extract_instrumental, use_tta, demud_phaseremix_inst, clean_model)

    # İşlem tamamlandıktan sonra giriş dizinini temizle 

    return result    


def clean_model_name(model):
    """
    Clean and standardize model names for filename
    """
    # Mapping of complex model names to simpler, filename-friendly versions
    model_name_mapping = {
        'VOCALS-InstVocHQ': 'InstVocHQ',
        'VOCALS-MelBand-Roformer (by KimberleyJSN)': 'KimberleyJSN',
        'VOCALS-BS-Roformer_1297 (by viperx)': 'VOCALS_BS_Roformer1297',
        'VOCALS-BS-Roformer_1296 (by viperx)': 'VOCALS-BS-Roformer_1296',
        'VOCALS-BS-RoformerLargev1 (by unwa)': 'UnwaLargeV1',
        'VOCALS-Mel-Roformer big beta 4 (by unwa)': 'UnwaBigBeta4',
        'VOCALS-Melband-Roformer BigBeta5e (by unwa)': 'UnwaBigBeta5e',
        'INST-Mel-Roformer v1 (by unwa)': 'UnwaInstV1',
        'INST-Mel-Roformer v2 (by unwa)': 'UnwaInstV2',
        'INST-VOC-Mel-Roformer a.k.a. duality (by unwa)': 'UnwaDualityV1',
        'INST-VOC-Mel-Roformer a.k.a. duality v2 (by unwa)': 'UnwaDualityV2',
        'KARAOKE-MelBand-Roformer (by aufr33 & viperx)': 'KaraokeMelBandRoformer',
        'VOCALS-VitLarge23 (by ZFTurbo)': 'VitLarge23',
        'VOCALS-MelBand-Roformer (by Becruily)': 'BecruilyVocals',
        'INST-MelBand-Roformer (by Becruily)': 'BecruilyInst',
        'VOCALS-MelBand-Roformer Kim FT (by Unwa)': 'KimFT',
        'INST-MelBand-Roformer Kim FT (by Unwa)': 'KimFTInst',
        'OTHER-BS-Roformer_1053 (by viperx)': 'OtherViperx1053',
        'CROWD-REMOVAL-MelBand-Roformer (by aufr33)': 'CrowdRemovalRoformer',
        'CINEMATIC-BandIt_Plus (by kwatcharasupat)': 'CinematicBandItPlus',
        'DRUMSEP-MDX23C_DrumSep_6stem (by aufr33 & jarredou)': 'DrumSepMDX23C',
        '4STEMS-SCNet_MUSDB18 (by starrytong)': 'FourStemsSCNet',
        'DE-REVERB-MDX23C (by aufr33 & jarredou)': 'DeReverbMDX23C',
        'DENOISE-MelBand-Roformer-1 (by aufr33)': 'DenoiseMelBand1',
        'DENOISE-MelBand-Roformer-2 (by aufr33)': 'DenoiseMelBand2',
        'INST-MelBand-Roformer (by Becruily)': 'BecruilyInst',
        '4STEMS-SCNet_XL_MUSDB18 (by ZFTurbo)': 'FourStemsSCNetXL',
        '4STEMS-SCNet_Large (by starrytong)': 'FourStemsSCNetLarge',
        '4STEMS-BS-Roformer_MUSDB18 (by ZFTurbo)': 'FourStemsBSRoformer',
        'DE-REVERB-MelBand-Roformer aggr./v2/19.1729 (by anvuew)': 'DeReverbMelBandAggr',
        'DE-REVERB-Echo-MelBand-Roformer (by Sucial)': 'DeReverbEchoMelBand',
        'bleed_suppressor_v1 (by unwa)': 'BleedSuppressorV1',
        'inst_v1e (by unwa)': 'InstV1E',
        'inst_gabox ( by Gabox)': 'InstGabox',
        'inst_gaboxBV1 (by Gabox)': 'InstGaboxBV1',
        'inst_gaboxBV2 (by Gabox)': 'InstGaboxBV2',
        'inst_gaboxBFV1 (by Gabox)': 'InstGaboxBFV1',
        'inst_gaboxFV2 (by Gabox)': 'InstGaboxFV2',
        'inst_gaboxFV1 (by Gabox)': 'InstGaboxFV1',
        'dereverb_mel_band_roformer_less_aggressive_anvuew': 'DereverbMelBandRoformerLessAggressive',
        'dereverb_mel_band_roformer_anvuew': 'DereverbMelBandRoformer',
        'VOCALS-Male Female-BS-RoFormer Male Female Beta 7_2889 (by aufr33)': 'MaleFemale-BS-RoFormer-(by aufr33)',
        'VOCALS-MelBand-Roformer (by Becruily)': 'Vocals-MelBand-Roformer-(by Becruily)',
        'VOCALS-MelBand-Roformer Kim FT 2 (by Unwa)': 'Vocals-MelBand-Roformer-KİM-FT-2(by Unwa)',
        'voc_gaboxMelRoformer (by Gabox)': 'voc_gaboxMelRoformer',
        'voc_gaboxBSroformer (by Gabox)': 'voc_gaboxBSroformer',
        'voc_gaboxMelRoformerFV1 (by Gabox)': 'voc_gaboxMelRoformerFV1',
        'voc_gaboxMelRoformerFV2 (by Gabox)': 'voc_gaboxMelRoformerFV2',
        'SYH99999/MelBandRoformerSYHFTB1(by Amane)': 'MelBandRoformerSYHFTB1',
        'inst_V5 (by Gabox)': 'INSTV5-(by Gabox)',
        'inst_Fv4Noise (by Gabox)': 'Inst_Fv4Noise-(by Gabox)',
        'Intrumental_Gabox (by Gabox)': 'Intrumental_Gabox-(by Gabox)',
        'inst_GaboxFv3 (by Gabox)': 'INST_GaboxFv3-(by Gabox)',
        'SYH99999/MelBandRoformerSYHFTB1_Model1 (by Amane)': 'MelBandRoformerSYHFTB1_model1',
        'SYH99999/MelBandRoformerSYHFTB1_Model2 (by Amane)': 'MelBandRoformerSYHFTB1_model2',
        'SYH99999/MelBandRoformerSYHFTB1_Model3 (by Amane)': 'MelBandRoformerSYHFTB1_model3',
        'VOCALS-MelBand-Roformer Kim FT 2 Blendless (by unwa)': 'VOCALS-MelBand-Roformer-Kim-FT-2-Blendless-(by unwa)',
        'inst_gaboxFV6 (by Gabox)': 'inst_gaboxFV6-(by Gabox)',
        'denoisedebleed (by Gabox)': 'denoisedebleed-(by Gabox)',
        'INSTV5N (by Gabox)': 'INSTV5N_(by Gabox)',
        'Voc_Fv3 (by Gabox)': 'Voc_Fv3_(by Gabox)',
        'MelBandRoformer4StemFTLarge (SYH99999)': 'MelBandRoformer4StemFTLarge_(SYH99999)',
        'dereverb_mel_band_roformer_mono (by anvuew)': 'dereverb_mel_band_roformer_mono_(by anvuew)',
        'INSTV6N (by Gabox)': 'INSTV6N_(by Gabox)',
        'KaraokeGabox': 'KaraokeGabox',
        'FullnessVocalModel (by Amane)': 'FullnessVocalModel',
        'Inst_GaboxV7 (by Gabox)': 'Inst_GaboxV7_(by Gabox)',
        
        # Add more mappings as needed
    }

    # Use mapping if exists, otherwise clean the model name
    if model in model_name_mapping:
        return model_name_mapping[model]
    
    # General cleaning if not in mapping
    cleaned = re.sub(r'\s*\(.*?\)', '', model)  # Remove parenthetical info
    cleaned = cleaned.replace('-', '_')
    cleaned = ''.join(char for char in cleaned if char.isalnum() or char == '_')
    
    return cleaned

def shorten_filename(filename, max_length=30):
    """
    Shortens a filename to a specified maximum length
    
    Args:
        filename (str): The filename to be shortened
        max_length (int): Maximum allowed length for the filename
    
    Returns:
        str: Shortened filename
    """
    base, ext = os.path.splitext(filename)
    if len(base) <= max_length:
        return filename
    
    # Take first 15 and last 10 characters
    shortened = base[:15] + "..." + base[-10:] + ext
    return shortened

def clean_filename(filename):
    """
    Temizlenmiş dosya adını döndürür
    """
    # Zaman damgası ve gereksiz etiketleri temizleme desenleri
    cleanup_patterns = [
        r'_\d{8}_\d{6}_\d{6}$',  # _20231215_123456_123456
        r'_\d{14}$',              # _20231215123456
        r'_\d{10}$',              # _1702658400
        r'_\d+$'                  # Herhangi bir sayı
    ]
    
    # Dosya adını ve uzantısını ayır
    base, ext = os.path.splitext(filename)
    
    # Zaman damgalarını temizle
    for pattern in cleanup_patterns:
        base = re.sub(pattern, '', base)
    
    # Dosya türü etiketlerini temizle
    file_types = ['vocals', 'instrumental', 'drum', 'bass', 'other', 'effects', 'speech', 'music', 'dry', 'male', 'female']
    for type_keyword in file_types:
        base = base.replace(f'_{type_keyword}', '')
    
    # Dosya türünü tespit et
    detected_type = None
    for type_keyword in file_types:
        if type_keyword in base.lower():
            detected_type = type_keyword
            break
    
    # Zaman damgaları ve gereksiz etiketlerden temizlenmiş base
    clean_base = base.strip('_- ')
    
    return clean_base, detected_type, ext

def run_command_and_process_files(model_type, config_path, start_check_point, INPUT_DIR, OUTPUT_DIR, extract_instrumental, use_tta, demud_phaseremix_inst, clean_model):
    try:
        # Komut parçalarını oluştur
        cmd_parts = [
            "python", "inference.py",
            "--model_type", model_type,
            "--config_path", config_path,
            "--start_check_point", start_check_point,
            "--input_folder", INPUT_DIR,
            "--store_dir", OUTPUT_DIR,  # İşlenecek ses dosyasının yolu
        ]

        # Opsiyonel parametreleri ekle
        if extract_instrumental:
            cmd_parts.append("--extract_instrumental")

        if use_tta:
            cmd_parts.append("--use_tta")

        if demud_phaseremix_inst:
            cmd_parts.append("--demud_phaseremix_inst")

        # Komutu çalıştır
        process = subprocess.Popen(
            cmd_parts,
            cwd=BASE_PATH,
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            text=True,
            bufsize=1,
            universal_newlines=True
        )

        # Çıktıları gerçek zamanlı olarak yazdır
        for line in process.stdout:
            print(line.strip())

        for line in process.stderr:
            print(line.strip())

        process.wait()

        # Model adını temizle
        filename_model = clean_model_name(clean_model)

        # Çıktı dosyalarını al
        output_files = os.listdir(OUTPUT_DIR)

        # Dosya yeniden adlandırma fonksiyonu
        def rename_files_with_model(folder, filename_model):
            for filename in sorted(os.listdir(folder)):
                file_path = os.path.join(folder, filename)

                # Medya dosyası değilse atla
                if not any(filename.lower().endswith(ext) for ext in ['.mp3', '.wav', '.flac', '.aac', '.ogg', '.m4a']):
                    continue

                base, ext = os.path.splitext(filename)

                # Temiz base adı
                clean_base = base.strip('_- ')

                # Yeni dosya adını oluştur
                new_filename = f"{clean_base}_{filename_model}{ext}"

                new_file_path = os.path.join(folder, new_filename)
                os.rename(file_path, new_file_path)

        # Dosyaları yeniden adlandır
        rename_files_with_model(OUTPUT_DIR, filename_model)

        # Güncellenmiş dosya listesini al
        output_files = os.listdir(OUTPUT_DIR)

        # Dosya bulma fonksiyonu
        def find_file(keyword):
            matching_files = [
                os.path.join(OUTPUT_DIR, f) for f in output_files 
                if keyword in f.lower()
            ]
            return matching_files[0] if matching_files else None

        # Farklı dosya türlerini bul
        vocal_file = find_file('vocals')
        instrumental_file = find_file('instrumental')
        phaseremix_file = find_file('phaseremix')
        drum_file = find_file('drum')
        bass_file = find_file('bass')
        other_file = find_file('other')
        effects_file = find_file('effects')
        speech_file = find_file('speech')
        music_file = find_file('music')
        dry_file = find_file('dry')
        male_file = find_file('male')
        female_file = find_file('female')
        bleed_file = find_file('bleed')
        karaoke_file = find_file('karaoke')
        

        # Bulunan dosyaları döndür
        return (
            vocal_file or None,
            instrumental_file or None,
            phaseremix_file or None,
            drum_file or None,
            bass_file or None,
            other_file or None,
            effects_file or None,
            speech_file or None,
            music_file or None,
            dry_file or None,
            male_file or None,
            female_file or None,
            bleed_file or None,
            karaoke_file or None
            
        )

    except Exception as e:
        print(f"An error occurred: {e}")
        return (None,) * 14

       

def create_interface():
    # Let's define the model options in advance
    model_choices = {
        "Vocal Separation": [
            'FullnessVocalModel (by Amane)',
            'Voc_Fv3 (by Gabox)',
            'VOCALS-BS-Roformer_1297 (by viperx)',
            'VOCALS-BS-Roformer_1296 (by viperx)',
            '✅ VOCALS-Mel-Roformer big beta 4 (by unwa) - Melspectrogram based high performance',
            'VOCALS-BS-RoformerLargev1 (by unwa) - Comprehensive model',
            'VOCALS-InstVocHQ - General purpose model',
            'VOCALS-MelBand-Roformer (by KimberleyJSN) - Alternative model',
            'VOCALS-VitLarge23 (by ZFTurbo) - Transformer-based model',
            'VOCALS-MelBand-Roformer Kim FT (by Unwa)',
            'VOCALS-MelBand-Roformer (by Becruily)',
            '✅ VOCALS-Melband-Roformer BigBeta5e (by unwa)',
            'VOCALS-Male Female-BS-RoFormer Male Female Beta 7_2889 (by aufr33)',
            'VOCALS-MelBand-Roformer Kim FT 2 (by Unwa)',
            'voc_gaboxMelRoforner (by Gabox)',
            'voc_gaboxBSroformer (by Gabox)',
            'voc_gaboxMelRoformerFV1 (by Gabox)',
            'voc_gaboxMelRoformerFV2 (by Gabox)',
            'VOCALS-MelBand-Roformer Kim FT 2 Blendless (by unwa)'
        ],
        "Instrumental Separation": [
            'Inst_GaboxV7 (by Gabox)',
            'INSTV5N (by Gabox)',
            'inst_gaboxFV6 (by Gabox)',
            '✅ INST-Mel-Roformer v2 (by unwa) - Most recent instrumental separation model',
            '✅ inst_v1e (by unwa)',
            '✅ INST-Mel-Roformer v1 (by unwa) - Old instrumental separation model',
            'INST-MelBand-Roformer (by Becruily)',
            'inst_gaboxFV2 (by Gabox)',
            'inst_gaboxFV1 (by Gabox)',
            'inst_gaboxBV2 (by Gabox)',
            'inst_gaboxBV1 (by Gabox)',
            'inst_gabox (by Gabox)',
            '✅(?) inst_GaboxFv3 (by Gabox)',
            'Intrumental_Gabox (by Gabox)',
            '✅(?) inst_Fv4Noise (by Gabox)',
            '✅(?) inst_V5 (by Gabox)',
            'INST-VOC-Mel-Roformer a.k.a. duality v2 (by unwa) - Latest version instrumental separation',
            'INST-VOC-Mel-Roformer a.k.a. duality (by unwa) - Previous version',
            'INST-Separator MDX23C (by aufr33) - Alternative instrumental separation',
            'INSTV6N (by Gabox)'
        ],
        "Karaoke & Accompaniment": [
            '✅ KARAOKE-MelBand-Roformer (by aufr33 & viperx) - Advanced karaoke separation',
            'KaraokeGabox'
        ],
        "Noise & Effect Removal": [
            'denoisedebleed (by Gabox)',
            '🔇 DENOISE-MelBand-Roformer-1 (by aufr33) - Basic noise reduction',
            '🔉 DENOISE-MelBand-Roformer-2 (by aufr33) - Advanced noise reduction',
            'bleed_suppressor_v1 (by unwa) - dont use it if you dont know what youre doing',
            'dereverb_mel_band_roformer_mono (by anvuew)',
            '👥 CROWD-REMOVAL-MelBand-Roformer (by aufr33) - Crowd noise removal',
            '🏛️ DE-REVERB-MDX23C (by aufr33 & jarredou) - Reverb reduction',
            '🏛️ DE-REVERB-MelBand-Roformer aggr./v2/19.1729 (by anvuew)',
            '🗣️ DE-REVERB-Echo-MelBand-Roformer (by Sucial)',
            'dereverb_mel_band_roformer_less_aggressive_anvuew',
            'dereverb_mel_band_roformer_anvuew'


        ],
        "Drum Separation": [
            '✅ DRUMSEP-MDX23C_DrumSep_6stem (by aufr33 & jarredou) - Detailed drum separation'
        ],
        "Multi-Stem & Other Models": [
            'MelBandRoformer4StemFTLarge (SYH99999)',
            '🎬 4STEMS-SCNet_MUSDB18 (by starrytong) - Multi-stem separation',
            '🎼 CINEMATIC-BandIt_Plus (by kwatcharasupat) - Cinematic music analysis',
            'OTHER-BS-Roformer_1053 (by viperx) - Other special models',
            '4STEMS-SCNet_XL_MUSDB18 (by ZFTurbo)',
            '4STEMS-SCNet_Large (by starrytong)',
            '4STEMS-BS-Roformer_MUSDB18 (by ZFTurbo)',
            'SYH99999/MelBandRoformerSYHFTB1_Model1 (by Amane)',
            'SYH99999/MelBandRoformerSYHFTB1_Model2 (by Amane)',
            'SYH99999/MelBandRoformerSYHFTB1_Model3 (by Amane)'
        ],
    }


    def update_models(category):
        models = model_choices.get(category, [])
        return gr.Dropdown(
            label="Select Model",
            choices=models,
            value=models[0] if models else None
        )


    def ensemble_files(args):
        """
        Ensemble audio files using the external script
        
        Args:
            args (list): Command-line arguments for ensemble script
        """
        try:
            
            script_path = "/content/Music-Source-Separation-Training/ensemble.py"
            
            
            full_command = ["python", script_path] + args
            
            
            result = subprocess.run(
                full_command,
                capture_output=True,
                text=True,
                check=True
            )
            
            print("Ensemble successful:")
            print(result.stdout)
            return result.stdout
        
        except subprocess.CalledProcessError as e:
            print(f"Ensemble error: {e}")
            print(f"Error output: {e.stderr}")
            raise
        except Exception as e:
            print(f"Unexpected error during ensemble: {e}")
            raise      

    def refresh_audio_files(directory):
        """
        Refreshes and lists audio files in the specified directory and old_output directory.
        
        Args:
            directory (str): Path of the directory to be scanned.
        
        Returns:
            list: List of discovered audio files.
        """
        try:
            audio_extensions = ['.wav', '.mp3', '.flac', '.ogg']
            audio_files = [
                f for f in os.listdir(directory)
                if os.path.isfile(os.path.join(directory, f))
                and os.path.splitext(f)[1].lower() in audio_extensions
            ]
            
            # Eski dosyaları da kontrol et
            old_output_directory = os.path.join(BASE_PATH, 'old_output')
            old_audio_files = [
                f for f in os.listdir(old_output_directory)
                if os.path.isfile(os.path.join(old_output_directory, f))
                and os.path.splitext(f)[1].lower() in audio_extensions
            ]
            
            return sorted(audio_files + old_audio_files)
        except Exception as e:
            print(f"Audio file listing error: {e}")
            return []

    
    # Global değişken tanımlamaları
    BASE_PATH = '/content/Music-Source-Separation-Training'
    AUTO_ENSEMBLE_TEMP = os.path.join(BASE_PATH, 'auto_ensemble_temp')
    model_output_dir = os.path.join(BASE_PATH, 'auto_ensemble_temp')

    def auto_ensemble_process(audio_input, selected_models, chunk_size, overlap, export_format2, 
                         use_tta, extract_instrumental, ensemble_type, 
                         progress=gr.Progress(), *args, **kwargs):
        try:
            # Ensure the ensemble directory exists
            move_wav_files2(INPUT_DIR)
            create_directory(ENSEMBLE_DIR)

            # Handle audio input
            if audio_input is not None:
                temp_path = audio_input.name  # Gradio'nun geçici dosya yolu
                audio_path = os.path.join(ENSEMBLE_DIR, os.path.basename(temp_path))
            else:
                existing_files = os.listdir(ENSEMBLE_DIR)
                if not existing_files:
                    return None, "❌ No audio file found"
                audio_path = os.path.join(ENSEMBLE_DIR, existing_files[0])

            # Model processing
            all_outputs = []
            total_models = len(selected_models)

            for idx, model in enumerate(selected_models):
                progress((idx + 1) / total_models, f"Processing {model}...")
                
                clean_model = extract_model_name(model)
                print(f"Processing using model: {clean_model}")      

                # Model output directory
                model_output_dir = os.path.join(AUTO_ENSEMBLE_TEMP, clean_model)
                os.makedirs(model_output_dir, exist_ok=True)

                model_type, config_path, start_check_point = "", "", ""

                if clean_model == 'VOCALS-InstVocHQ':
                        model_type = 'mdx23c'
                        config_path = 'ckpts/config_vocals_mdx23c.yaml'
                        start_check_point = 'ckpts/model_vocals_mdx23c_sdr_10.17.ckpt'
                        download_file('https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/config_vocals_mdx23c.yaml')
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.0/model_vocals_mdx23c_sdr_10.17.ckpt')

                elif clean_model == 'VOCALS-MelBand-Roformer (by KimberleyJSN)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_vocals_mel_band_roformer_kj.yaml'
                        start_check_point = 'ckpts/MelBandRoformer.ckpt'
                        download_file('https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/KimberleyJensen/config_vocals_mel_band_roformer_kj.yaml')
                        download_file('https://huggingface.co/KimberleyJSN/melbandroformer/resolve/main/MelBandRoformer.ckpt')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'VOCALS-BS-Roformer_1297 (by viperx)':
                        model_type = 'bs_roformer'
                        config_path = 'ckpts/model_bs_roformer_ep_317_sdr_12.9755.yaml'
                        start_check_point = 'ckpts/model_bs_roformer_ep_317_sdr_12.9755.ckpt'
                        download_file('https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/viperx/model_bs_roformer_ep_317_sdr_12.9755.yaml')
                        download_file('https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/model_bs_roformer_ep_317_sdr_12.9755.ckpt')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'VOCALS-BS-Roformer_1296 (by viperx)':
                        model_type = 'bs_roformer'
                        config_path = 'ckpts/model_bs_roformer_ep_368_sdr_12.9628.yaml'
                        start_check_point = 'ckpts/model_bs_roformer_ep_368_sdr_12.9628.ckpt'
                        download_file('https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/model_bs_roformer_ep_368_sdr_12.9628.ckpt')
                        download_file('https://raw.githubusercontent.com/TRvlvr/application_data/main/mdx_model_data/mdx_c_configs/model_bs_roformer_ep_368_sdr_12.9628.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'VOCALS-BS-RoformerLargev1 (by unwa)':
                        model_type = 'bs_roformer'
                        config_path = 'ckpts/config_bsrofoL.yaml'
                        start_check_point = 'ckpts/BS-Roformer_LargeV1.ckpt'
                        download_file('https://huggingface.co/jarredou/unwa_bs_roformer/resolve/main/BS-Roformer_LargeV1.ckpt')
                        download_file('https://huggingface.co/jarredou/unwa_bs_roformer/raw/main/config_bsrofoL.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'VOCALS-Mel-Roformer big beta 4 (by unwa)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_melbandroformer_big_beta4.yaml'
                        start_check_point = 'ckpts/melband_roformer_big_beta4.ckpt'
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-big/resolve/main/melband_roformer_big_beta4.ckpt')
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-big/raw/main/config_melbandroformer_big_beta4.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'VOCALS-Melband-Roformer BigBeta5e (by unwa)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/big_beta5e.yaml'
                        start_check_point = 'ckpts/big_beta5e.ckpt'
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-big/resolve/main/big_beta5e.ckpt')
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-big/resolve/main/big_beta5e.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'INST-Mel-Roformer v1 (by unwa)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_melbandroformer_inst.yaml'
                        start_check_point = 'ckpts/melband_roformer_inst_v1.ckpt'
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/melband_roformer_inst_v1.ckpt')
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/raw/main/config_melbandroformer_inst.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'INST-Mel-Roformer v2 (by unwa)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_melbandroformer_inst_v2.yaml'
                        start_check_point = 'ckpts/melband_roformer_inst_v2.ckpt'
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/melband_roformer_inst_v2.ckpt')
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/raw/main/config_melbandroformer_inst_v2.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'INST-VOC-Mel-Roformer a.k.a. duality (by unwa)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_melbandroformer_instvoc_duality.yaml'
                        start_check_point = 'ckpts/melband_roformer_instvoc_duality_v1.ckpt'
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/resolve/main/melband_roformer_instvoc_duality_v1.ckpt')
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/raw/main/config_melbandroformer_instvoc_duality.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'INST-VOC-Mel-Roformer a.k.a. duality v2 (by unwa)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_melbandroformer_instvoc_duality.yaml'
                        start_check_point = 'ckpts/melband_roformer_instvox_duality_v2.ckpt'
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/resolve/main/melband_roformer_instvox_duality_v2.ckpt')
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/raw/main/config_melbandroformer_instvoc_duality.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'KARAOKE-MelBand-Roformer (by aufr33 & viperx)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_mel_band_roformer_karaoke.yaml'
                        start_check_point = 'ckpts/mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt'
                        download_file('https://huggingface.co/jarredou/aufr33-viperx-karaoke-melroformer-model/resolve/main/mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt')
                        download_file('https://huggingface.co/jarredou/aufr33-viperx-karaoke-melroformer-model/resolve/main/config_mel_band_roformer_karaoke.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'OTHER-BS-Roformer_1053 (by viperx)':
                        model_type = 'bs_roformer'
                        config_path = 'ckpts/model_bs_roformer_ep_937_sdr_10.5309.yaml'
                        start_check_point = 'ckpts/model_bs_roformer_ep_937_sdr_10.5309.ckpt'
                        download_file('https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/model_bs_roformer_ep_937_sdr_10.5309.ckpt')
                        download_file('https://raw.githubusercontent.com/TRvlvr/application_data/main/mdx_model_data/mdx_c_configs/model_bs_roformer_ep_937_sdr_10.5309.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'CROWD-REMOVAL-MelBand-Roformer (by aufr33)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/model_mel_band_roformer _crowd.yaml'
                        start_check_point = 'ckpts/mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt'
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.4/mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt')
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.4/model_mel_band_roformer_crowd.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'VOCALS-VitLarge23 (by ZFTurbo)':
                        model_type = 'segm_models'
                        config_path = 'ckpts/config_vocals_segm_models.yaml'
                        start_check_point = 'ckpts/model_vocals_segm_models_sdr_9.77.ckpt'
                        download_file('https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/refs/heads/main/configs/config_vocals_segm_models.yaml')
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.0/model_vocals_segm_models_sdr_9.77.ckpt')

                elif clean_model == 'CINEMATIC-BandIt_Plus (by kwatcharasupat)':
                        model_type = 'bandit'
                        config_path = 'ckpts/config_dnr_bandit_bsrnn_multi_mus64.yaml'
                        start_check_point = 'ckpts/model_bandit_plus_dnr_sdr_11.47.chpt'
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.3/config_dnr_bandit_bsrnn_multi_mus64.yaml')
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.3/model_bandit_plus_dnr_sdr_11.47.chpt')

                elif clean_model == 'DRUMSEP-MDX23C_DrumSep_6stem (by aufr33 & jarredou)':
                        model_type = 'mdx23c'
                        config_path = 'ckpts/aufr33-jarredou_DrumSep_model_mdx23c_ep_141_sdr_10.8059.yaml'
                        start_check_point = 'ckpts/aufr33-jarredou_DrumSep_model_mdx23c_ep_141_sdr_10.8059.ckpt'
                        download_file('https://github.com/jarredou/models/releases/download/aufr33-jarredou_MDX23C_DrumSep_model_v0.1/aufr33-jarredou_DrumSep_model_mdx23c_ep_141_sdr_10.8059.ckpt')
                        download_file('https://github.com/jarredou/models/releases/download/aufr33-jarredou_MDX23C_DrumSep_model_v0.1/aufr33-jarredou_DrumSep_model_mdx23c_ep_141_sdr_10.8059.yaml')

                elif clean_model == '4STEMS-SCNet_MUSDB18 (by starrytong)':
                        model_type = 'scnet'
                        config_path = 'ckpts/config_musdb18_scnet.yaml'
                        start_check_point = 'ckpts/scnet_checkpoint_musdb18.ckpt'
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.6/config_musdb18_scnet.yaml')
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.6/scnet_checkpoint_musdb18.ckpt')

                elif clean_model == 'DE-REVERB-MDX23C (by aufr33 & jarredou)':
                        model_type = 'mdx23c'
                        config_path = 'ckpts/config_dereverb_mdx23c.yaml'
                        start_check_point = 'ckpts/dereverb_mdx23c_sdr_6.9096.ckpt'
                        download_file('https://huggingface.co/jarredou/aufr33_jarredou_MDXv3_DeReverb/resolve/main/dereverb_mdx23c_sdr_6.9096.ckpt')
                        download_file('https://huggingface.co/jarredou/aufr33_jarredou_MDXv3_DeReverb/resolve/main/config_dereverb_mdx23c.yaml')

                elif clean_model == 'DENOISE-MelBand-Roformer-1 (by aufr33)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/model_mel_band_roformer_denoise.yaml'
                        start_check_point = 'ckpts/denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt'
                        download_file('https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt')
                        download_file('https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/model_mel_band_roformer_denoise.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'DENOISE-MelBand-Roformer-2 (by aufr33)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/model_mel_band_roformer_denoise.yaml'
                        start_check_point = 'ckpts/denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt'
                        download_file('https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt')
                        download_file('https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/model_mel_band_roformer_denoise.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'VOCALS-MelBand-Roformer Kim FT (by Unwa)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_kimmel_unwa_ft.yaml'
                        start_check_point = 'ckpts/kimmel_unwa_ft.ckpt'
                        download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/kimmel_unwa_ft.ckpt')
                        download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/config_kimmel_unwa_ft.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'inst_v1e (by unwa)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_melbandroformer_inst.yaml'
                        start_check_point = 'ckpts/inst_v1e.ckpt'
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/inst_v1e.ckpt')
                        download_file('https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/config_melbandroformer_inst.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'bleed_suppressor_v1 (by unwa)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_bleed_suppressor_v1.yaml'
                        start_check_point = 'ckpts/bleed_suppressor_v1.ckpt'
                        download_file('https://huggingface.co/ASesYusuf1/MODELS/resolve/main/bleed_suppressor_v1.ckpt')
                        download_file('https://huggingface.co/ASesYusuf1/MODELS/resolve/main/config_bleed_suppressor_v1.yaml')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'VOCALS-MelBand-Roformer (by Becruily)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_instrumental_becruily.yaml'
                        start_check_point = 'ckpts/mel_band_roformer_vocals_becruily.ckpt'
                        download_file('https://huggingface.co/becruily/mel-band-roformer-vocals/resolve/main/config_vocals_becruily.yaml')
                        download_file('https://huggingface.co/becruily/mel-band-roformer-vocals/resolve/main/mel_band_roformer_vocals_becruily.ckpt')
                        conf_edit(config_path, chunk_size, overlap)
                
                elif clean_model == 'INST-MelBand-Roformer (by Becruily)':
                        model_type = 'mel_band_roformer'
                        config_path = 'ckpts/config_instrumental_becruily.yaml'
                        start_check_point = 'ckpts/mel_band_roformer_instrumental_becruily.ckpt'
                        download_file('https://huggingface.co/becruily/mel-band-roformer-instrumental/resolve/main/config_instrumental_becruily.yaml')
                        download_file('https://huggingface.co/becruily/mel-band-roformer-instrumental/resolve/main/mel_band_roformer_instrumental_becruily.ckpt')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == '4STEMS-SCNet_XL_MUSDB18 (by ZFTurbo)':
                        model_type = 'scnet'
                        config_path = 'ckpts/config_musdb18_scnet_xl.yaml'
                        start_check_point = 'ckpts/model_scnet_ep_54_sdr_9.8051.ckpt'
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.13/config_musdb18_scnet_xl.yaml')
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.13/model_scnet_ep_54_sdr_9.8051.ckpt')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == '4STEMS-SCNet_Large (by starrytong)':
                        model_type = 'scnet'
                        config_path = 'ckpts/config_musdb18_scnet_large_starrytong.yaml'
                        start_check_point = 'ckpts/SCNet-large_starrytong_fixed.ckpt'
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.9/config_musdb18_scnet_large_starrytong.yaml')
                        download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.9/SCNet-large_starrytong_fixed.ckpt')
                        conf_edit(config_path, chunk_size, overlap)

                elif clean_model == '4STEMS-BS-Roformer_MUSDB18 (by ZFTurbo)':
                      model_type = 'bs_roformer'
                      config_path = 'ckpts/config_bs_roformer_384_8_2_485100.yaml'
                      start_check_point = 'ckpts/model_bs_roformer_ep_17_sdr_9.6568.ckpt'
                      download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.12/config_bs_roformer_384_8_2_485100.yaml')
                      download_file('https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.12/model_bs_roformer_ep_17_sdr_9.6568.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'DE-REVERB-MelBand-Roformer aggr./v2/19.1729 (by anvuew)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/dereverb_mel_band_roformer_anvuew.yaml'
                      start_check_point = 'ckpts/dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt'
                      download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt')
                      download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml')
                      conf_edit(config_path, chunk_size, overlap)
                      
                elif clean_model == 'DE-REVERB-Echo-MelBand-Roformer (by Sucial)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/config_dereverb-echo_mel_band_roformer.yaml'
                      start_check_point = 'ckpts/dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt'
                      download_file('https://huggingface.co/Sucial/Dereverb-Echo_Mel_Band_Roformer/resolve/main/dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt')
                      download_file('https://huggingface.co/Sucial/Dereverb-Echo_Mel_Band_Roformer/resolve/main/config_dereverb-echo_mel_band_roformer.yaml')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'dereverb_mel_band_roformer_less_aggressive_anvuew':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/dereverb_mel_band_roformer_anvuew.yaml'
                      start_check_point = 'ckpts/dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt'
                      download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml')
                      download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'dereverb_mel_band_roformer_anvuew':
                      model_type = 'mel_band_roformer'
                      config_path = 'dereverb_mel_band_roformer_anvuew.yaml'
                      start_check_point = 'ckpts/dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt'
                      download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml')
                      download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt')
                      conf_edit(config_path, chunk_size, overlap)  


                elif clean_model == 'inst_gabox (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/inst_gabox.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'inst_gaboxBV1 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/inst_gaboxBv1.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxBv1.ckpt')
                      conf_edit(config_path, chunk_size, overlap)


                elif clean_model == 'inst_gaboxBV2 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/inst_gaboxBv2.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxBv2.ckpt')
                      conf_edit(config_path, chunk_size, overlap)     


                elif clean_model == 'inst_gaboxBFV1 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/gaboxFv1.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv1.ckpt')
                      conf_edit(config_path, chunk_size, overlap)


                elif clean_model == 'inst_gaboxFV2 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/inst_gaboxFv2.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv2.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                
                elif clean_model == 'VOCALS-Male Female-BS-RoFormer Male Female Beta 7_2889 (by aufr33)':
                      model_type = 'bs_roformer'
                      config_path = 'ckpts/config_chorus_male_female_bs_roformer.yaml'
                      start_check_point = 'ckpts/bs_roformer_male_female_by_aufr33_sdr_7.2889.ckpt'
                      download_file('https://huggingface.co/RareSirMix/AIModelRehosting/resolve/main/bs_roformer_male_female_by_aufr33_sdr_7.2889.ckpt')
                      download_file('https://huggingface.co/Sucial/Chorus_Male_Female_BS_Roformer/resolve/main/config_chorus_male_female_bs_roformer.yaml')
                      conf_edit(config_path, chunk_size, overlap)


                elif clean_model == 'VOCALS-MelBand-Roformer Kim FT 2 (by Unwa)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/config_kimmel_unwa_ft.yaml'
                      start_check_point = 'ckpts/kimmel_unwa_ft2.ckpt'
                      download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/config_kimmel_unwa_ft.yaml')
                      download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/kimmel_unwa_ft2.ckpt')
                      conf_edit(config_path, chunk_size, overlap)      

                elif clean_model == 'voc_gaboxBSroformer (by Gabox)':
                      model_type = 'bs_roformer'
                      config_path = 'ckpts/voc_gaboxBSroformer.yaml'
                      start_check_point = 'ckpts/voc_gaboxBSR.ckpt'
                      download_file('https://huggingface.co/GaboxR67/BSRoformerVocTest/resolve/main/voc_gaboxBSroformer.yaml')
                      download_file('https://huggingface.co/GaboxR67/BSRoformerVocTest/resolve/main/voc_gaboxBSR.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'voc_gaboxMelReformer (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/voc_gabox.yaml'
                      start_check_point = 'ckpts/voc_gabox.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'voc_gaboxMelReformerFV1 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/voc_gabox.yaml'
                      start_check_point = 'ckpts/voc_gaboxFv1.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gaboxFv1.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'voc_gaboxMelReformerFV2 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/voc_gabox.yaml'
                      start_check_point = 'ckpts/voc_gaboxFv2.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gaboxFv2.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'inst_GaboxFv3 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/inst_gaboxFv3.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv3.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'Intrumental_Gabox (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/intrumental_gabox.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/intrumental_gabox.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'inst_Fv4Noise (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/inst_Fv4Noise.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_Fv4Noise.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'inst_V5 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/INSTV5.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV5.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'SYH99999/MelBandRoformerSYHFTB1_Model1 (by Amane)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/config.yaml'
                      start_check_point = 'ckpts/model.ckpt'
                      download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/config.yaml')
                      download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/model.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'SYH99999/MelBandRoformerSYHFTB1_Model2 (by Amane)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/config.yaml'
                      start_check_point = 'ckpts/model2.ckpt'
                      download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/config.yaml')
                      download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/model2.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'SYH99999/MelBandRoformerSYHFTB1_Model3 (by Amane)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/config.yaml'
                      start_check_point = 'ckpts/model3.ckpt'
                      download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/config.yaml')
                      download_file('https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/model3.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'VOCALS-MelBand-Roformer Kim FT 2 Blendless (by unwa)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/config_kimmel_unwa_ft.yaml'
                      start_check_point = 'ckpts/kimmel_unwa_ft2_bleedless.ckpt'
                      download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/config_kimmel_unwa_ft.yaml')
                      download_file('https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/kimmel_unwa_ft2_bleedless.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'inst_gaboxFV1 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/inst_gaboxFv1.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv1.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'inst_gaboxFV6 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/INSTV6.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV6.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'denoisedebleed (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/model_mel_band_roformer_denoise.yaml'
                      start_check_point = 'ckpts/denoisedebleed.ckpt'
                      download_file('https://huggingface.co/poiqazwsx/melband-roformer-denoise/resolve/main/model_mel_band_roformer_denoise.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/denoisedebleed.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'INSTV5N (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/INSTV5N.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV5N.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'Voc_Fv3 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/voc_gabox.yaml'
                      start_check_point = 'ckpts/voc_Fv3.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_Fv3.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'MelBandRoformer4StemFTLarge (SYH99999)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/config.yaml'
                      start_check_point = 'ckpts/MelBandRoformer4StemFTLarge.ckpt'
                      download_file('https://huggingface.co/SYH99999/MelBandRoformer4StemFTLarge/resolve/main/config.yaml')
                      download_file('https://huggingface.co/SYH99999/MelBandRoformer4StemFTLarge/resolve/main/MelBandRoformer4StemFTLarge.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'dereverb_mel_band_roformer_mono (by anvuew)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/dereverb_mel_band_roformer_anvuew.yaml'
                      start_check_point = 'ckpts/dereverb_mel_band_roformer_mono_anvuew_sdr_20.4029.ckpt'
                      download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml')
                      download_file('https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_mono_anvuew_sdr_20.4029.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'INSTV6N (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/INSTV6N.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV6N.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'KaraokeGabox':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/config_mel_band_roformer_karaoke.yaml'
                      start_check_point = 'ckpts/KaraokeGabox.ckpt'
                      download_file('https://github.com/deton24/Colab-for-new-MDX_UVR_models/releases/download/v1.0.0/config_mel_band_roformer_karaoke.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/blob/main/melbandroformers/experimental/KaraokeGabox.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'FullnessVocalModel (by Amane)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/config.yaml'
                      start_check_point = 'ckpts/FullnessVocalModel.ckpt'
                      download_file('https://huggingface.co/Aname-Tommy/MelBandRoformers/blob/main/config.yaml')
                      download_file('https://huggingface.co/Aname-Tommy/MelBandRoformers/blob/main/FullnessVocalModel.ckpt')
                      conf_edit(config_path, chunk_size, overlap)

                elif clean_model == 'Inst_GaboxV7 (by Gabox)':
                      model_type = 'mel_band_roformer'
                      config_path = 'ckpts/inst_gabox.yaml'
                      start_check_point = 'ckpts/Inst_GaboxV7.ckpt'
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml')
                      download_file('https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/Inst_GaboxV7.ckpt')
                      conf_edit(config_path, chunk_size, overlap)        

              

                # Ana sekme komut yapısını kullan
                cmd = [
                    "python", 
                    "inference.py",
                    "--model_type", model_type,
                    "--config_path", config_path,
                    "--start_check_point", start_check_point,
                    "--input_folder", ENSEMBLE_DIR,
                    "--store_dir", model_output_dir,
                ]

                if use_tta:
                    cmd.append("--use_tta")
                if extract_instrumental:
                    cmd.append("--extract_instrumental")

                print(f"Running command: {' '.join(cmd)}")

                # Hata yakalama ile çalıştırma
                try:
                    result = subprocess.run(cmd, capture_output=True, text=True)
                    print(result.stdout)
                    if result.returncode != 0:
                        print(f"Error: {result.stderr}")
                        return None, f"Model {model} failed: {result.stderr}"
                except Exception as e:
                    return None, f"Critical error with {model}: {str(e)}"

                    # Çıktı dosyalarını topla
                model_outputs = glob.glob(os.path.join(model_output_dir, "*.wav"))
                all_outputs.extend(model_outputs)

                    # 3. Çıktı dosyalarını kontrol et
                output_files = glob.glob(os.path.join(model_output_dir, "*.wav"))
                if not output_files:
                    raise FileNotFoundError(f"{model} failed to produce output")
                    
                model_outputs.extend(output_files)
                

            # 4. Dosya bekletme ve kontrol
            def wait_for_files(files, timeout=300):
                start = time.time()
                while time.time() - start < timeout:
                    missing = [f for f in files if not os.path.exists(f)]
                    if not missing: return True
                    time.sleep(5)
                raise TimeoutError(f"Missing files: {missing[:3]}...")

            wait_for_files(model_outputs)

            # 5. Ensemble komutunu güvenli oluştur
            quoted_files = [f'"{f}"' for f in model_outputs]
            timestamp = str(int(time.time()))
            output_path = os.path.join(AUTO_ENSEMBLE_OUTPUT, f"ensemble_{timestamp}.wav")
            
            ensemble_cmd = [
                "python", "ensemble.py",
                "--files", *quoted_files,
                "--type", ensemble_type,
                "--output", f'"{output_path}"'
            ]

            # 6. Komutu çalıştır
            result = subprocess.run(
                " ".join(ensemble_cmd),
                shell=True,
                capture_output=True,
                text=True,
                check=True
            )

            # 7. Son kontrol
            if not os.path.exists(output_path):
                raise RuntimeError("Ensemble dosyası oluşturulamadı")
                
            return output_path, "✅ Success!"

        except Exception as e:
            return None, f"❌ Error: {str(e)}"
        
        finally:
            # Temizlik
            shutil.rmtree('/content/Music-Source-Separation-Training/ensemble', ignore_errors=True)
            shutil.rmtree('/content/Music-Source-Separation-Training/ensemble', ignore_errors=True)
            clear_directory(VİDEO_TEMP)
            clear_directory(ENSEMBLE_DIR)
            gc.collect()
             

    main_input_key = "shared_audio_input"
    # Global components
    input_audio_file = gr.File(visible=True)
    auto_input_audio_file = gr.File(visible=True)
    original_audio = gr.Audio(visible=True)
    

    css = """
    /* Genel Tema */
    body {
        background: url('/content/logo.jpg') no-repeat center center fixed;
        background-size: cover;
        background-color: #2d0b0b; /* Koyu kırmızı, dublaj stüdyosuna uygun */
        min-height: 100vh;
        margin: 0;
        padding: 1rem;
        font-family: 'Poppins', sans-serif;
        color: #C0C0C0; /* Metalik gümüş metin, profesyonel görünüm */
    }

    body::after {
        content: '';
        position: fixed;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
        background: rgba(45, 11, 11, 0.9); /* Daha koyu kırmızı overlay */
        z-index: -1;
    }

    /* Logo Stilleri */
    .logo-container {
        position: absolute;
        top: 1rem;
        left: 50%;
        transform: translateX(-50%);
        display: flex;
        align-items: center;
        z-index: 2000; /* Diğer öğelerden üstte, mutlaka görünür */
    }

    .logo-img {
        width: 120px;
        height: auto;
    }

    /* Başlık Stilleri */
    .header-text {
      
        text-align: center;
        padding: 80px 20px 20px; /* Logo için alan bırak */
        color: #ff4040; /* Kırmızı, dublaj temasına uygun */
        font-size: 2.5rem; /* Daha etkileyici ve büyük başlık */
        font-weight: 900; /* Daha kalın ve dramatik */
        text-shadow: 0 0 10px rgba(255, 64, 64, 0.5); /* Kırmızı gölge efekti */
        z-index: 1500; /* Tablerden üstte, logonun altında */
    }

    /* Metalik kırmızı parlama animasyonu */
    @keyframes metallic-red-shine {
        0% { filter: brightness(1) saturate(1) drop-shadow(0 0 5px #ff4040); }
        50% { filter: brightness(1.3) saturate(1.7) drop-shadow(0 0 15px #ff6b6b); }
        100% { filter: brightness(1) saturate(1) drop-shadow(0 0 5px #ff4040); }
    }

    /* Dublaj temalı stil */
    .dubbing-theme {
        background: linear-gradient(to bottom, #800000, #2d0b0b); /* Koyu kırmızı gradyan */
        border-radius: 15px;
        padding: 1rem;
        box-shadow: 0 10px 20px rgba(255, 64, 64, 0.3); /* Kırmızı gölge */
    }

    /* Footer Stilleri (Tablerin Üstünde, Şeffaf) */
    .footer {
        text-align: center;
        padding: 10px;
        color: #ff4040; /* Kırmızı metin, dublaj temasına uygun */
        font-size: 14px;
        margin-top: 20px;
        position: relative;
        z-index: 1001; /* Tablerden üstte, logodan düşük */
    }

    /* Düğme ve Yükleme Alanı Stilleri */
    button {
        transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
        background: #800000 !important; /* Koyu kırmızı, dublaj temasına uygun */
        border: 1px solid #ff4040 !important; /* Kırmızı sınır */
        color: #C0C0C0 !important; /* Metalik gümüş metin */
        border-radius: 8px !important;
        padding: 8px 16px !important;
        position: relative;
        overflow: hidden !important;
        font-size: 0.9rem !important;
    }

    button:hover {
        transform: scale(1.05) !important;
        box-shadow: 0 10px 40px rgba(255, 64, 64, 0.7) !important; /* Daha belirgin kırmızı gölge */
        background: #ff4040 !important; /* Daha açık kırmızı hover efekti */
    }

    button::before {
        content: '';
        position: absolute;
        top: -50%;
        left: -50%;
        width: 200%;
        height: 200%;
        background: linear-gradient(45deg, 
            transparent 20%, 
            rgba(192, 192, 192, 0.3) 50%, /* Metalik gümüş ton */
            transparent 80%);
        animation: button-shine 3s infinite linear;
    }

    /* Resim ve Ses Yükleme Alanı Stili */
    .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; /* Kırmızı sınır */
        background: rgba(128, 0, 0, 0.5) !important; /* Koyu kırmızı, şeffaf */
        border-radius: 8px !important;
        transition: all 0.2s ease !important;
        color: #C0C0C0 !important; /* Metalik gümüş metin */
    }

    .compact-upload.horizontal:hover {
        border-color: #ff6b6b !important; /* Daha açık kırmızı */
        background: rgba(128, 0, 0, 0.7) !important; /* Daha koyu kırmızı hover */
    }

    .compact-upload.horizontal .w-full {
        flex: 1 1 auto !important;
        min-width: 120px !important;
        margin: 0 !important;
        color: #C0C0C0 !important; /* Metalik gümüş */
    }

    .compact-upload.horizontal button {
        padding: 4px 12px !important;
        font-size: 0.75em !important;
        height: 28px !important;
        min-width: 80px !important;
        border-radius: 4px !important;
        background: #800000 !important; /* Koyu kırmızı */
        border: 1px solid #ff4040 !important; /* Kırmızı sınır */
        color: #C0C0C0 !important; /* Metalik gümüş */
    }

    .compact-upload.horizontal .text-gray-500 {
        font-size: 0.7em !important;
        color: rgba(192, 192, 192, 0.6) !important; /* Şeffaf metalik gümüş */
        white-space: nowrap !important;
        overflow: hidden !important;
        text-overflow: ellipsis !important;
        max-width: 180px !important;
    }

    /* Ekstra Dar Versiyon */
    .compact-upload.horizontal.x-narrow {
        max-width: 320px !important;
        height: 36px !important;
        padding: 0 10px !important;
        gap: 6px !important;
    }

    .compact-upload.horizontal.x-narrow button {
        padding: 3px 10px !important;
        font-size: 0.7em !important;
        height: 26px !important;
        min-width: 70px !important;
    }

    .compact-upload.horizontal.x-narrow .text-gray-500 {
        font-size: 0.65em !important;
        max-width: 140px !important;
    }

    /* Sekmeler İçin Ortak Stiller */
    .gr-tab {
        background: rgba(128, 0, 0, 0.5) !important; /* Koyu kırmızı, şeffaf */
        border-radius: 12px 12px 0 0 !important;
        margin: 0 5px !important;
        color: #C0C0C0 !important; /* Metalik gümüş */
        border: 1px solid #ff4040 !important; /* Kırmızı sınır */
        z-index: 1500; /* Logo’nun altında, diğer öğelerden üstte */
    }

    .gr-tab-selected {
        background: #800000 !important; /* Koyu kırmızı */
        box-shadow: 0 4px 12px rgba(255, 64, 64, 0.7) !important; /* Daha belirgin kırmızı gölge */
        color: #ffffff !important; /* Beyaz metin (seçili sekme için kontrast) */
        border: 1px solid #ff6b6b !important; /* Daha açık kırmızı */
    }

    /* Manuel Ensemble Özel Stilleri */
    .compact-header {
        font-size: 0.95em !important;
        margin: 0.8rem 0 0.5rem 0 !important;
        color: #C0C0C0 !important; /* Metalik gümüş metin */
    }

    .compact-grid {
        gap: 0.4rem !important;
        max-height: 50vh;
        overflow-y: auto;
        padding: 10px;
        background: rgba(128, 0, 0, 0.3) !important; /* Koyu kırmızı, şeffaf */
        border-radius: 12px;
        border: 1px solid #ff4040 !important; /* Kırmızı sınır */
    }

    .compact-dropdown {
        --padding: 8px 12px !important;
        --radius: 10px !important;
        border: 1px solid #ff4040 !important; /* Kırmızı sınır */
        background: rgba(128, 0, 0, 0.5) !important; /* Koyu kırmızı, şeffaf */
        color: #C0C0C0 !important; /* Metalik gümüş metin */
    }

    .tooltip-icon {
        font-size: 1.4em !important;
        color: #C0C0C0 !important; /* Metalik gümüş */
        cursor: help;
        margin-left: 0.5rem !important;
    }

    .log-box {
        font-family: 'Fira Code', monospace !important;
        font-size: 0.85em !important;
        background-color: rgba(128, 0, 0, 0.3) !important; /* Koyu kırmızı, şeffaf */
        border: 1px solid #ff4040 !important; /* Kırmızı sınır */
        border-radius: 8px;
        padding: 1rem !important;
        color: #C0C0C0 !important; /* Metalik gümüş metin */
    }

    /* Animasyonlar */
    @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); }
    }

    @keyframes button-shine {
        0% { transform: rotate(0deg) translateX(-50%); }
        100% { transform: rotate(360deg) translateX(-50%); }
    }

    /* Responsive Ayarlar */
    @media (max-width: 768px) {
        .compact-grid {
            max-height: 40vh;
        }

        .compact-upload.horizontal {
            max-width: 100% !important;
            width: 100% !important;
        }
        
        .compact-upload.horizontal .text-gray-500 {
            max-width: 100px !important;
        }
        
        .compact-upload.horizontal.x-narrow {
            height: 40px !important;
            padding: 0 8px !important;
        }

        .logo-container {
            width: 80px; /* Mobil cihazlarda daha küçük logo */
            top: 1rem;
            left: 50%;
            transform: translateX(-50%);
        }

        .header-text {
            padding: 60px 20px 20px; /* Mobil için daha az boşluk */
            font-size: 1.8rem; /* Mobil için biraz daha küçük başlık */
        }
    }
    """

    # Arayüz tasarımı
    with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
        with gr.Column():
            # Logo (PNG olarak, dublaj temasına uygun)
            logo_html = """
            <div class="logo-container">
                <img src="/content/gk_logo.png" alt="" class="logo-img">
            </div>
            """
            gr.HTML(logo_html)

            # Başlık (Etkileyici ve dublaj temalı)
            gr.HTML("""
            <div class="header-text">
                Gecekondu Dubbing Production
            </div>
            """)
            
        with gr.Tabs():
            with gr.Tab("Audio Separation", elem_id="separation_tab"):
                with gr.Row(equal_height=True):
                    # Sol Panel - Kontroller
                    with gr.Column(scale=1, min_width=380):
                        with gr.Accordion("📥 Input & Model", open=True):
                            with gr.Tabs():
                                with gr.Tab("🖥 Upload"):
                                    input_audio_file = gr.File(
                                        file_types=[".wav", ".mp3", ".m4a", ".mp4", ".mkv", ".flac"],
                                        elem_classes=["compact-upload", "horizontal", "x-narrow"],
                                        label="",
                                        scale=1
                                    )

                                with gr.Tab("📂 Path"):
                                    file_path_input = gr.Textbox(placeholder="/path/to/audio.wav")
             
                            
                            with gr.Row():
                                model_category = gr.Dropdown(
                                    label="Category",
                                    choices=list(model_choices.keys()),
                                    value="Vocal Separation"
                                )
                                model_dropdown = gr.Dropdown(label="Model")

                        with gr.Accordion("⚙ Settings", open=False):
                            with gr.Row():
                                export_format = gr.Dropdown(
                                    label="Format",
                                    choices=['wav FLOAT', 'flac PCM_16', 'flac PCM_24'],
                                    value='wav FLOAT'
                                )
                                chunk_size = gr.Dropdown(
                                    label="Chunk Size",
                                    choices=[352800, 485100],
                                    value=352800,
                                    info="Don't change unless you have specific requirements"
                                )
                            
                            with gr.Row():
                                overlap = gr.Slider(2, 50, step=1, label="Overlap")
                                info="Recommended: 2-10 (Higher values increase quality but require more VRAM)"
                                use_tta = gr.Checkbox(label="TTA Boost")
                                info="Improves quality but increases processing time"

                            with gr.Row():
                                use_demud_phaseremix_inst = gr.Checkbox(label="Phase Fix")
                                info="Advanced phase correction for instrumental tracks"
                                extract_instrumental = gr.Checkbox(label="Instrumental")

                        with gr.Row():
                            process_btn = gr.Button("🚀 Process", variant="primary")
                            clear_old_output_btn = gr.Button("🧹 Reset", variant="secondary")
                            clear_old_output_status = gr.Textbox(label="Status", interactive=False)

                    # Sağ Panel - Sonuçlar
                    with gr.Column(scale=2, min_width=800):
                        with gr.Tabs():
                            with gr.Tab("🎧 Main"):
                                with gr.Column():
                                    original_audio = gr.Audio(label="Original", interactive=False)
                                    with gr.Row():
                                        vocals_audio = gr.Audio(label="Vocals", show_download_button=True)
                                        instrumental_audio = gr.Audio(label="Instrumental", show_download_button=True)

                            with gr.Tab("🔍 Details"):
                                with gr.Column():
                                    with gr.Row():
                                        male_audio = gr.Audio(label="Male")
                                        female_audio = gr.Audio(label="Female")
                                        speech_audio = gr.Audio(label="Speech")
                                    with gr.Row():
                                        drum_audio = gr.Audio(label="Drums")
                                        bass_audio = gr.Audio(label="Bass")
                                    with gr.Row():
                                        other_audio = gr.Audio(label="Other")
                                        effects_audio = gr.Audio(label="Effects")

                            with gr.Tab("⚙ Advanced"):
                                with gr.Column():
                                    with gr.Row():
                                        phaseremix_audio = gr.Audio(label="Phase Remix")
                                        dry_audio = gr.Audio(label="Dry")
                                    with gr.Row():
                                        music_audio = gr.Audio(label="Music")
                                        karaoke_audio = gr.Audio(label="Karaoke")
                                        bleed_audio = gr.Audio(label="Bleed") 
                       
                            with gr.Row():
                        
                                gr.Markdown("""
                                <div style="
                                    background: rgba(245,245,220,0.15);
                                    padding: 1rem;
                                    border-radius: 8px;
                                    border-left: 3px solid #6c757d;
                                    margin: 1rem 0 1.5rem 0;
                                ">
                                    <b>🔈 Processing Tip:</b> For noisy results, use <code>bleed_suppressor_v1</code> 
                                    or <code>denoisedebleed</code> models in the <i>"Denoise & Effect Removal"</i> 
                                    category to clean the output
                                </div>
                                """)                                        
                        



            # Oto Ensemble Sekmesi
            with gr.Tab("Auto Ensemble"):
                with gr.Row():
                    with gr.Column():
                        with gr.Group():
                            auto_input_audio_file = gr.File(label="Upload file")
                            auto_file_path_input = gr.Textbox(
                                label="Or enter file path",
                                placeholder="Enter full path to audio file",
                                interactive=True
                            )

                        with gr.Accordion("⚙️ Advanced Settings", open=False):
                            with gr.Row():
                                auto_use_tta = gr.Checkbox(label="Use TTA", value=False)
                                auto_extract_instrumental = gr.Checkbox(label="Instrumental Only")
                            
                            with gr.Row():
                                auto_overlap = gr.Slider(
                                    label="Overlap",
                                    minimum=2,
                                    maximum=50,
                                    value=2,
                                    step=1
                                )
                                auto_chunk_size = gr.Dropdown(
                                    label="Chunk Size",
                                    choices=[352800, 485100],
                                    value=352800
                                )
                                export_format2 = gr.Dropdown(
                                    label="Output Format",
                                    choices=['wav FLOAT', 'flac PCM_16', 'flac PCM_24'],
                                    value='wav FLOAT'
                                )

                        # Model Seçim Bölümü
                        with gr.Group():
                            gr.Markdown("### 🧠 Model Selection") 
                            with gr.Row():
                                auto_category_dropdown = gr.Dropdown(
                                label="Model Category",
                                choices=list(model_choices.keys()),
                                value="Vocal Separation"
                            )

                            # Model seçimi (tek seferde)
                            auto_model_dropdown = gr.Dropdown(
                                label="Select Models from Category",
                                choices=model_choices["Vocal Separation"],
                                multiselect=True,
                                max_choices=50,
                                interactive=True
                            )

                            # Seçilen modellerin listesi (ayrı kutucuk)
                            selected_models = gr.Dropdown(
                                label="Selected Models",
                                choices=[],
                                multiselect=True,
                                interactive=False  # Kullanıcı buraya direkt seçim yapamaz
                            )

                            
                            with gr.Row():
                                add_btn = gr.Button("➕ Add Selected", variant="secondary")
                                clear_btn = gr.Button("🗑️ Clear All", variant="stop")

                        # Ensemble Ayarları
                        with gr.Group():
                            gr.Markdown("### ⚡ Ensemble Settings")
                            with gr.Row():
                                auto_ensemble_type = gr.Dropdown(
                                    label="Method",
                                    choices=['avg_wave', 'median_wave', 'min_wave', 'max_wave',
                                          'avg_fft', 'median_fft', 'min_fft', 'max_fft'],
                                    value='avg_wave'
                                )
                            
                            gr.Markdown("**Recommendation:** avg_wave and max_fft best results")

                        auto_process_btn = gr.Button("🚀 Start Processing", variant="primary")

                    with gr.Column():
                        with gr.Tabs():
                            with gr.Tab("🔊 Original Audio"):
                                original_audio2 = gr.Audio(
                                        label=" Original Audio",
                                        interactive=False,
                                        every=1,  # Her 1 saniyede bir güncelle
                                        elem_id="original_audio_player"
                                    )
                            with gr.Tab("🎚️ Ensemble Result"):
                                auto_output_audio = gr.Audio(
                                    label="Output Preview",
                                    show_download_button=True,
                                    interactive=False
                                )
                        
                        auto_status = gr.Textbox(
                            label="Processing Status",
                            interactive=False,
                            placeholder="Waiting for processing...",
                            elem_classes="status-box"
                        )

                        gr.Markdown("""
                            <div style="
                                background: rgba(110, 142, 251, 0.1);
                                padding: 1.2rem;
                                border-radius: 12px;
                                border-left: 4px solid #6e8efb;
                                margin: 1rem 0;
                                backdrop-filter: blur(3px);
                                border: 1px solid rgba(255,255,255,0.2);
                            ">
                                <div style="display: flex; align-items: start; gap: 1rem;">
                                    <div style="
                                        font-size: 1.4em;
                                        color: #6e8efb;
                                        margin-top: -2px;
                                    ">⚠️</div>
                                    <div style="color: #2d3748;">
                                        <h4 style="
                                            margin: 0 0 0.8rem 0;
                                            color: #4a5568;
                                            font-weight: 600;
                                            font-size: 1.1rem;
                                        ">
                                            Model Selection Guidelines
                                        </h4>
                                        <ul style="
                                            margin: 0;
                                            padding-left: 1.2rem;
                                            color: #4a5568;
                                            line-height: 1.6;
                                        ">
                                            <li><strong>Avoid cross-category mixing:</strong> Combining vocal and instrumental models may create unwanted blends</li>
                                            <li><strong>Special model notes:</strong>
                                                <ul style="padding-left: 1.2rem; margin: 0.5rem 0;">
                                                    <li>Duality models (v1/v2) - Output both stems</li>
                                                    <li>MDX23C Separator - Hybrid results</li>
                                                </ul>
                                            </li>
                                            <li><strong>Best practice:</strong> Use 3-5 similar models from same category</li>
                                        </ul>
                                        <div style="
                                            margin-top: 1rem;
                                            padding: 0.8rem;
                                            background: rgba(167, 119, 227, 0.1);
                                            border-radius: 8px;
                                            color: #6e8efb;
                                            font-size: 0.9rem;
                                        ">
                                            💡 Pro Tip: Start with "VOCALS-MelBand-Roformer BigBeta5e" + "VOCALS-BS-Roformer_1297" combination
                                        </div>
                                    </div>
                                </div>
                            </div>
                            """)

                # Kategori değişim fonksiyonunu güncelleyelim
                def update_models(category):
                    return gr.Dropdown(choices=model_choices[category])

                def add_models(new_models, existing_models):
                    updated = list(set(existing_models + new_models))
                    return gr.Dropdown(choices=updated, value=updated)

                def clear_models():
                    return gr.Dropdown(choices=[], value=[]) 

                # Etkileşimler
                def update_category(target):
                    category_map = {
                        "Only Vocals": "Vocal Separation",
                        "Only Instrumental": "Instrumental Separation"
                    }
                    return category_map.get(target, "Vocal Separation")

                # Otomatik yenileme için olayı bağla
                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=update_models,
                    inputs=auto_category_dropdown,
                    outputs=auto_model_dropdown
                )

                add_btn.click(
                     fn=add_models,
                     inputs=[auto_model_dropdown, selected_models],
                     outputs=selected_models
                )

                clear_btn.click(
                     fn=clear_models,
                     inputs=[],
                     outputs=selected_models
                )

                auto_process_btn.click(
                    fn=auto_ensemble_process,
                    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)
                    ],
                    outputs=[auto_output_audio, auto_status]
                )                        

            # İndirme Sekmesi
            with gr.Tab("Download Sources"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### 🗂️ Cloud Storage")
                        drive_url_input = gr.Textbox(label="Google Drive Shareable Link")
                        drive_download_btn = gr.Button("⬇️ Download from Drive", variant="secondary")
                        drive_download_status = gr.Textbox(label="Download Status")
                        drive_download_output = gr.File(label="Downloaded File", interactive=False)

                    with gr.Column():
                        gr.Markdown("### 🌐 Direct Links")
                        direct_url_input = gr.Textbox(label="Audio File URL")
                        direct_download_btn = gr.Button("⬇️ Download from URL", variant="secondary")
                        direct_download_status = gr.Textbox(label="Download Status")
                        direct_download_output = gr.File(label="Downloaded File", interactive=False)

                    with gr.Column():
                        gr.Markdown("### 🍪 Cookie Management")
                        cookie_file = gr.File(
                            label="Upload Cookies.txt",
                            file_types=[".txt"],
                            interactive=True,
                            elem_id="cookie_upload"
                        )
                        gr.Markdown("""
                        <div style="margin-left:15px; font-size:0.95em">
                        
                        **📌 Why Needed?**  
                        - Access age-restricted content  
                        - Download private/unlisted videos  
                        - Bypass regional restrictions  
                        - Avoid YouTube download limits  

                        **⚠️ Important Notes**  
                        - NEVER share your cookie files!  
                        - Refresh cookies when:  
                          • Getting "403 Forbidden" errors  
                          • Downloads suddenly stop  
                          • Seeing "Session expired" messages  

                        **🔄 Renewal Steps**  
                        1. Install this <a href="https://chromewebstore.google.com/detail/get-cookiestxt-clean/ahmnmhfbokciafffnknlekllgcnafnie" target="_blank">Chrome extension</a>  
                        2. Login to YouTube in Chrome  
                        3. Click extension icon → "Export"  
                        4. Upload the downloaded file here  

                        **⏳ Cookie Lifespan**  
                        - Normal sessions: 24 hours  
                        - Sensitive operations: 1 hour  
                        - Password changes: Immediate invalidation  

                        </div>
                        """)


                        # Event handlers
                        model_category.change(
                            fn=update_models,
                            inputs=model_category,
                            outputs=model_dropdown
                        )

                        clear_old_output_btn.click(
                            fn=clear_old_output,
                            outputs=clear_old_output_status
                        )

                        process_btn.click(
                            fn=process_audio,
                            inputs=[
                                input_audio_file,
                                model_dropdown,
                                chunk_size,
                                overlap,
                                export_format,
                                use_tta,
                                use_demud_phaseremix_inst,
                                extract_instrumental,
                                gr.State(None),
                                gr.State(None)
                            ],
                            outputs=[
                                vocals_audio, instrumental_audio, phaseremix_audio,
                                drum_audio, karaoke_audio, bass_audio, other_audio, effects_audio,
                                speech_audio, bleed_audio, music_audio, dry_audio, male_audio, female_audio
                            ]
                        )

                        drive_download_btn.click(
                            fn=download_callback,
                            inputs=[drive_url_input, gr.State('drive')],
                            outputs=[
                                drive_download_output,  # 0. Dosya çıktısı
                                drive_download_status,  # 1. Durum mesajı
                                input_audio_file,       # 2. Ana ses dosyası girişi
                                auto_input_audio_file,  # 3. Oto ensemble girişi
                                original_audio,         # 4. Orijinal ses çıktısı
                                original_audio2 
                            ]
                        )

                        direct_download_btn.click(
                            fn=download_callback,
                            inputs=[direct_url_input, gr.State('direct'), cookie_file],
                            outputs=[
                                direct_download_output,  # 0. Dosya çıktısı
                                direct_download_status,  # 1. Durum mesajı
                                input_audio_file,        # 2. Ana ses dosyası girişi
                                auto_input_audio_file,   # 3. Oto ensemble girişi
                                original_audio,           # 4. Orijinal ses çıktısı
                                original_audio2 
                            ]
                        )

            
            with gr.Tab("🎚️ Manuel Ensemble"):
                with gr.Row(equal_height=True):
                    # Sol Panel - Giriş ve Ayarlar
                    with gr.Column(scale=1, min_width=400):
                        with gr.Accordion("📂 Input Sources", open=True):
                            with gr.Row():
                                refresh_btn = gr.Button("🔄 Refresh", variant="secondary", size="sm")
                                ensemble_type = gr.Dropdown(
                                label="Ensemble Algorithm",
                                choices=[
                                    'avg_wave',
                                    'median_wave',
                                    'min_wave',
                                    'max_wave',
                                    'avg_fft',
                                    'median_fft',
                                    'min_fft',
                                    'max_fft'
                                ],
                                value='avg_wave'
                            )
                            
                            # Dosya listesini belirli bir yoldan al
                            file_path = "/content/drive/MyDrive/output"  # Sabit yol
                            initial_files = glob.glob(f"{file_path}/*.wav") + glob.glob("/content/Music-Source-Separation-Training/old_output/*.wav")
                            
                            gr.Markdown("### Select Audio Files")
                            file_dropdown = gr.Dropdown(
                                choices=initial_files,
                                label="Available Files",
                                multiselect=True,
                                interactive=True,
                                elem_id="file-dropdown"
                            )
                            
                            weights_input = gr.Textbox(
                                label="Custom Weights (comma separated)",
                                placeholder="Example: 0.8, 1.2, 1.0, ...",
                                info="Leave empty for equal weights"
                            )

                    # Sağ Panel - Sonuçlar
                    with gr.Column(scale=2, min_width=800):
                        with gr.Tabs():
                            with gr.Tab("🎧 Result Preview"):
                                ensemble_output_audio = gr.Audio(
                                    label="Ensembled Output",
                                    interactive=False,
                                    show_download_button=True,
                                    elem_id="output-audio"
                                )
                            
                            with gr.Tab("📋 Processing Log"):
                                ensemble_status = gr.Textbox(
                                    label="Processing Details",
                                    interactive=False,
                                    elem_id="log-box"
                                )

                            with gr.Row(): 

                                ensemble_process_btn = gr.Button(
                                    "⚡ Process Ensemble", 
                                    variant="primary",
                                    size="sm",  # Boyutu küçülttüm
                                    elem_id="process-btn"
                                )

                # Etkileşimler
                def update_file_list():
                    files = glob.glob(f"{file_path}/*.wav") + glob.glob("/content/Music-Source-Separation-Training/old_output/*.wav")
                    return gr.Dropdown(choices=files)

                refresh_btn.click(
                    fn=update_file_list,
                    outputs=file_dropdown
                )
                
                def ensemble_audio_fn(files, method, weights):
                    try:
                        if len(files) < 2:
                            return None, "⚠️ Minimum 2 files required"
                        
                        # Dosya yollarını kontrol et
                        valid_files = [f for f in files if os.path.exists(f)]
                        
                        if len(valid_files) < 2:
                            return None, "❌ Valid files not found"
                        
                        # Create output directory if needed
                        output_dir = "/content/drive/MyDrive/ensembles"
                        os.makedirs(output_dir, exist_ok=True)  # This line fixes the error
                        
                        # Create output path
                        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
                        output_path = f"{output_dir}/ensemble_{timestamp}.wav"
                        
                        # Ensemble işlemi
                        ensemble_args = [
                            "--files", *valid_files,
                            "--type", method.lower().replace(' ', '_'),
                            "--output", output_path
                        ]
                        
                        if weights and weights.strip():
                            weights_list = [str(w) for w in map(float, weights.split(','))]
                            ensemble_args += ["--weights", *weights_list]
                        
                        result = subprocess.run(
                            ["python", "ensemble.py"] + ensemble_args,
                            capture_output=True,
                            text=True
                        )
                        
                        log = f"✅ Success!\n{result.stdout}" if not result.stderr else f"❌ Error!\n{result.stderr}"
                        return output_path, log

                    except Exception as e:
                        return None, f"⛔ Critical Error: {str(e)}"

                ensemble_process_btn.click(
                    fn=ensemble_audio_fn,
                    inputs=[file_dropdown, ensemble_type, weights_input],
                    outputs=[ensemble_output_audio, ensemble_status]
                )
        
        gr.HTML("""
        <div class="footer">
            Presented by Gecekondu Production
        </div>
        """)

    return demo

def launch_with_share():
    try:
        port = generate_random_port()
        demo = create_interface()
        
        share_link = demo.launch(
            share=True,
            server_port=port,
            server_name='0.0.0.0',
            inline=False,
            allowed_paths=[
                '/content',
                '/content/drive/MyDrive/output',
                '/tmp'
                '/model_output_dir',
                'model_output_dir'
            ]
        )
        
        print(f"🌐 Gradio Share Link: {share_link}")
        print(f"🔌 Local Server Port: {port}")
        
        while True:
            time.sleep(1)

    except KeyboardInterrupt:
        print("🛑 Server stopped by user.")
    except Exception as e:
        print(f"❌ Error during server launch: {e}")
    finally:
        try:
            demo.close()
        except:
            pass

if __name__ == "__main__":
    launch_with_share()