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README.md

@@ -1,12 +1,3 @@
----
-title: Gecekondu Dubbing Studio
-emoji: 👁
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 5.20.0
-app_file: app.py
-pinned: false
----
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Gecekondu Dubbing Production Space
+Bu Space, ses ayrıştırma ve dublaj işlemleri için profesyonel bir arayüz sunar.

app.py

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+
+import gradio as gr
+from gui import create_interface
+
+interface = create_interface()
+interface.launch(server_name="0.0.0.0", server_port=7860)

download.py

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+import os
+import re
+import validators
+import yt_dlp
+import torch
+import gdown
+from urllib.parse import quote
+from helpers import INPUT_DIR, COOKIE_PATH, clear_directory, clear_temp_folder, BASE_DIR
+import yaml
+
+def download_callback(url, download_type='direct', cookie_file=None):
+    clear_temp_folder("/tmp", exclude_items=["gradio", "config.json"])
+    clear_directory(INPUT_DIR)
+    os.makedirs(INPUT_DIR, exist_ok=True)
+
+    if not validators.url(url):
+        return None, "❌ Invalid URL", None, None, None, None
+
+    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
+
+    if download_type == 'drive':
+        try:
+            file_id = re.search(r'/d/([^/]+)', url).group(1) if '/d/' in url else url.split('id=')[-1]
+            output_path = os.path.join(INPUT_DIR, "drive_download.wav")
+            gdown.download(f'https://drive.google.com/uc?id={file_id}', output_path, quiet=True)
+            if os.path.exists(output_path) and os.path.getsize(output_path) > 0:
+                wav_path = output_path
+                download_success = True
+            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:
+        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
+                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
+
+    if download_success and wav_path:
+        for f in os.listdir(INPUT_DIR):
+            if f != os.path.basename(wav_path):
+                os.remove(os.path.join(INPUT_DIR, f))
+        return (
+            wav_path,
+            "🎉 Downloaded successfully!",
+            wav_path,
+            wav_path,
+            wav_path,
+            wav_path
+        )
+    return None, "❌ Download failed", None, None, None, None

gui.py

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+import gradio as gr
+import os
+import glob
+import subprocess
+from pathlib import Path
+from datetime import datetime
+from helpers import update_model_dropdown, handle_file_upload, clear_old_output, save_uploaded_file, update_file_list
+from download import download_callback
+from model import get_model_config, MODEL_CONFIGS
+from processing import process_audio, auto_ensemble_process, ensemble_audio_fn
+
+# BASE_DIR tanımı (BASE_PATH yerine)
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+# Arayüz oluşturma fonksiyonu
+def create_interface():
+    # Gelişmiş ve profesyonel CSS
+    css = """
+    /* Genel Tema */
+    body {
+        background: url('/content/studio_bg.jpg') no-repeat center center fixed;
+        background-size: cover;
+        background-color: #1a0d0d; /* Daha koyu ve sofistike kırmızı ton */
+        min-height: 100vh;
+        margin: 0;
+        padding: 2rem;
+        font-family: 'Montserrat', sans-serif; /* Daha modern ve profesyonel font */
+        color: #D4A017; /* Altın tonu metin, lüks hissi */
+        overflow-x: hidden;
+    }
+
+    body::before {
+        content: '';
+        position: fixed;
+        top: 0;
+        left: 0;
+        width: 100%;
+        height: 100%;
+        background: linear-gradient(135deg, rgba(26, 13, 13, 0.9), rgba(45, 11, 11, 0.85));
+        z-index: -1;
+    }
+
+    /* Logo Stilleri */
+    .logo-container {
+        position: fixed;
+        top: 1.5rem;
+        left: 2rem;
+        display: flex;
+        align-items: center;
+        z-index: 3000;
+        background: rgba(0, 0, 0, 0.7);
+        padding: 0.5rem 1rem;
+        border-radius: 10px;
+        box-shadow: 0 4px 15px rgba(212, 160, 23, 0.3);
+    }
+
+    .logo-img {
+        width: 100px;
+        height: auto;
+        filter: drop-shadow(0 0 5px rgba(212, 160, 23, 0.5));
+    }
+
+    /* Başlık Stilleri */
+    .header-text {
+        text-align: center;
+        padding: 4rem 0 2rem;
+        color: #D4A017; /* Altın tonu, profesyonel ve dikkat çekici */
+        font-size: 3rem;
+        font-weight: 800;
+        text-transform: uppercase;
+        letter-spacing: 2px;
+        text-shadow: 0 0 15px rgba(212, 160, 23, 0.7), 0 0 5px rgba(255, 64, 64, 0.5);
+        z-index: 1500;
+        animation: text-glow 4s infinite ease-in-out;
+    }
+
+    /* Profesyonel Panel Stili */
+    .dubbing-panel {
+        background: rgba(26, 13, 13, 0.95);
+        border: 1px solid #D4A017;
+        border-radius: 12px;
+        padding: 1.5rem;
+        box-shadow: 0 8px 25px rgba(212, 160, 23, 0.2);
+        transition: transform 0.3s ease;
+    }
+
+    .dubbing-panel:hover {
+        transform: translateY(-5px);
+    }
+
+    /* Düğme Stilleri */
+    button {
+        background: linear-gradient(45deg, #800000, #A31818); /* Koyu kırmızıdan parlak kırmızıya */
+        border: 1px solid #D4A017 !important;
+        color: #FFF !important;
+        border-radius: 8px !important;
+        padding: 10px 20px !important;
+        font-weight: 600;
+        text-transform: uppercase;
+        letter-spacing: 1px;
+        transition: all 0.3s ease !important;
+        box-shadow: 0 4px 15px rgba(212, 160, 23, 0.3);
+    }
+
+    button:hover {
+        background: linear-gradient(45deg, #A31818, #D42F2F) !important;
+        transform: scale(1.05) !important;
+        box-shadow: 0 6px 20px rgba(212, 160, 23, 0.5) !important;
+    }
+
+    /* Yükleme Alanı */
+    .compact-upload.horizontal {
+        background: rgba(26, 13, 13, 0.9);
+        border: 1px solid #D4A017;
+        border-radius: 8px;
+        padding: 0.5rem 1rem;
+        display: flex;
+        align-items: center;
+        gap: 10px;
+        max-width: 450px;
+        transition: all 0.3s ease;
+    }
+
+    .compact-upload.horizontal:hover {
+        border-color: #FFD700; /* Parlak altın hover efekti */
+        background: rgba(45, 11, 11, 0.95);
+    }
+
+    .compact-upload.horizontal button {
+        background: #800000;
+        border: 1px solid #D4A017;
+        padding: 6px 12px;
+        font-size: 0.8rem;
+    }
+
+    /* Sekme Stilleri */
+    .gr-tab {
+        background: rgba(26, 13, 13, 0.9);
+        border: 1px solid #D4A017;
+        border-radius: 12px 12px 0 0;
+        padding: 0.75rem 1.5rem;
+        color: #D4A017;
+        font-weight: 600;
+        text-transform: uppercase;
+        transition: all 0.3s ease;
+    }
+
+    .gr-tab-selected {
+        background: #800000;
+        color: #FFF;
+        border-bottom: none;
+        box-shadow: 0 4px 15px rgba(212, 160, 23, 0.4);
+    }
+
+    /* Altbilgi */
+    .footer {
+        text-align: center;
+        padding: 1rem;
+        color: #D4A017;
+        font-size: 1rem;
+        font-weight: 500;
+        text-shadow: 0 0 5px rgba(212, 160, 23, 0.3);
+        position: relative;
+        z-index: 1001;
+    }
+
+    /* Animasyonlar */
+    @keyframes text-glow {
+        0% { text-shadow: 0 0 5px rgba(212, 160, 23, 0.5); }
+        50% { text-shadow: 0 0 20px rgba(212, 160, 23, 1), 0 0 10px rgba(255, 64, 64, 0.7); }
+        100% { text-shadow: 0 0 5px rgba(212, 160, 23, 0.5); }
+    }
+    """
+
+    # Arayüz tasarımı
+    with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
+        # Üst Panel: Logo ve Başlık
+        with gr.Row():
+            with gr.Column(scale=1, min_width=200):
+                gr.HTML("""
+                <div class="logo-container">
+                    <img src="/content/gk_logo.png" alt="Gecekondu Dubbing" class="logo-img">
+                </div>
+                """)
+            with gr.Column(scale=4):
+                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=""
+                                    )
+
+                                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_CONFIGS.keys()),
+                                    value="Vocal Models"
+                                )
+                                model_dropdown = gr.Dropdown(
+                                    label="Model",
+                                    choices=list(MODEL_CONFIGS["Vocal Models"].keys())
+                                )
+
+                        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", value=2)
+                                gr.Markdown("Recommended: 2-10 (Higher values increase quality but require more VRAM)")
+                                use_tta = gr.Checkbox(label="TTA Boost")
+
+                            with gr.Row():
+                                use_demud_phaseremix_inst = gr.Checkbox(label="Phase Fix")
+                                gr.Markdown("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_CONFIGS.keys()),
+                                    value="Vocal Models"
+                                )
+
+                            # Model seçimi (tek seferde)
+                            auto_model_dropdown = gr.Dropdown(
+                                label="Select Models from Category",
+                                choices=list(MODEL_CONFIGS["Vocal Models"].keys()),
+                                multiselect=True,
+                                max_choices=50,
+                                interactive=True
+                            )
+
+                            # Seçilen modellerin listesi (ayrı kutucuk)
+                            selected_models = gr.Dropdown(
+                                label="Selected Models",
+                                choices=[],
+                                multiselect=True,
+                                interactive=False
+                            )
+
+                            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,
+                                    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>
+                            """)
+
+            # 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>
+                        """)
+
+            # Manuel Ensemble Sekmesi
+            with gr.Tab("🎚️ Manuel Ensemble"):
+                with gr.Row(equal_height=True):
+                    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 = os.path.join(Path.home(), 'Music-Source-Separation', 'output')
+                            initial_files = glob.glob(f"{file_path}/*.wav") + glob.glob(os.path.join(BASE_DIR, 'Music-Source-Separation-Training', 'old_output', '*.wav'))
+
+                            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",
+                                    elem_id="process-btn"
+                                )
+
+        gr.HTML("""
+        <div class="footer">
+            Presented by Gecekondu Production
+        </div>
+        """)
+
+        def update_models(category):
+            return gr.Dropdown(choices=list(MODEL_CONFIGS[category].keys()))
+
+        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=[])
+
+        # Event handlers
+        model_category.change(fn=update_model_dropdown, inputs=model_category, outputs=model_dropdown)
+        clear_old_output_btn.click(fn=clear_old_output, outputs=clear_old_output_status)
+
+        input_audio_file.upload(
+            fn=lambda x, y: handle_file_upload(x, y, is_auto_ensemble=False),
+            inputs=[input_audio_file, file_path_input],
+            outputs=[input_audio_file, original_audio]
+        )
+        file_path_input.change(
+            fn=lambda x, y: handle_file_upload(x, y, is_auto_ensemble=False),
+            inputs=[input_audio_file, file_path_input],
+            outputs=[input_audio_file, original_audio]
+        )
+
+        auto_input_audio_file.upload(
+            fn=lambda x, y: handle_file_upload(x, y, is_auto_ensemble=True),
+            inputs=[auto_input_audio_file, auto_file_path_input],
+            outputs=[auto_input_audio_file, original_audio2]
+        )
+        auto_file_path_input.change(
+            fn=lambda x, y: handle_file_upload(x, y, is_auto_ensemble=True),
+            inputs=[auto_input_audio_file, auto_file_path_input],
+            outputs=[auto_input_audio_file, original_audio2]
+        )
+
+        auto_category_dropdown.change(fn=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)
+
+        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
+            ]
+        )
+
+        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]
+        )
+
+        drive_download_btn.click(
+            fn=download_callback,
+            inputs=[drive_url_input, gr.State('drive')],
+            outputs=[drive_download_output, drive_download_status, input_audio_file, auto_input_audio_file, original_audio, original_audio2]
+        )
+
+        direct_download_btn.click(
+            fn=download_callback,
+            inputs=[direct_url_input, gr.State('direct'), cookie_file],
+            outputs=[direct_download_output, direct_download_status, input_audio_file, auto_input_audio_file, original_audio, original_audio2]
+        )
+
+        refresh_btn.click(fn=update_file_list, outputs=file_dropdown)
+        ensemble_process_btn.click(fn=ensemble_audio_fn, inputs=[file_dropdown, ensemble_type, weights_input], outputs=[ensemble_output_audio, ensemble_status])
+
+    return demo

helpers.py

@@ -0,0 +1,232 @@
+import os
+import shutil
+import glob
+import re
+import subprocess
+import random
+import yaml
+from pathlib import Path  # Doğru kullanım: Path (küçük harfli 'path' değil)
+import torch
+import gradio as gr
+import threading
+import time
+import librosa
+import soundfile as sf
+import numpy as np
+import requests
+import json
+import locale
+from datetime import datetime
+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 gc
+import psutil
+import concurrent.futures
+from tqdm import tqdm
+from google.oauth2.credentials import Credentials
+import tempfile
+from urllib.parse import urlparse, quote
+import gdown
+import argparse
+from tqdm.auto import tqdm
+import torch.nn as nn
+from model import get_model_config, MODEL_CONFIGS
+
+# Temel dizinler
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+INPUT_DIR = os.path.join(BASE_DIR, "input")
+OUTPUT_DIR = os.path.join(BASE_DIR, "output")
+OLD_OUTPUT_DIR = os.path.join(BASE_DIR, "old_output")
+AUTO_ENSEMBLE_TEMP = os.path.join(BASE_DIR, "auto_ensemble_temp")
+AUTO_ENSEMBLE_OUTPUT = os.path.join(BASE_DIR, "ensemble_folder")
+VIDEO_TEMP = os.path.join(BASE_DIR, "video_temp")
+ENSEMBLE_DIR = os.path.join(BASE_DIR, "ensemble")
+COOKIE_PATH = os.path.join(BASE_DIR, "cookies.txt")
+INFERENCE_SCRIPT_PATH = os.path.join(BASE_DIR, "inference.py")
+
+# Dizinleri oluştur
+for directory in [BASE_DIR, INPUT_DIR, OUTPUT_DIR, OLD_OUTPUT_DIR, AUTO_ENSEMBLE_TEMP, AUTO_ENSEMBLE_OUTPUT, VIDEO_TEMP, ENSEMBLE_DIR]:
+    os.makedirs(directory, exist_ok=True)
+
+# YAML için özel sınıf ve yapılandırıcı
+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):
+    """Loads a tuple from a YAML sequence."""
+    values = loader.construct_sequence(node)
+    return tuple(values)
+
+# PyYAML ile tuple constructor'ı kaydet
+yaml.SafeLoader.add_constructor('tag:yaml.org,2002:python/tuple', tuple_constructor)
+
+def update_model_dropdown(category):
+    """Kategoriye göre model dropdown'ını günceller."""
+    return gr.Dropdown(choices=list(MODEL_CONFIGS[category].keys()), label="Model")
+
+# Dosya yükleme işlevi (güncellenmiş)
+def handle_file_upload(uploaded_file, file_path, is_auto_ensemble=False):
+    clear_temp_folder("/tmp", exclude_items=["gradio", "config.json"])
+    clear_directory(INPUT_DIR)
+    os.makedirs(INPUT_DIR, exist_ok=True)
+    clear_directory(INPUT_DIR)
+    if uploaded_file:
+        # Yüklenen dosyayı INPUT_DIR'a kaydet
+        target_path = save_uploaded_file(uploaded_file, is_input=True)
+        return target_path, target_path
+    elif file_path and os.path.exists(file_path):
+        # Mevcut dosyayı INPUT_DIR'a kopyala
+        target_path = os.path.join(INPUT_DIR, os.path.basename(file_path))
+        shutil.copy(file_path, target_path)
+        return target_path, target_path
+    return None, None
+
+def clear_directory(directory):
+    """Deletes all files in the given directory."""
+    files = glob.glob(os.path.join(directory, '*'))
+    for f in files:
+        try:
+            os.remove(f)
+        except Exception as e:
+            print(f"{f} could not be deleted: {e}")
+
+def clear_temp_folder(folder_path, exclude_items=None):
+    """Safely clears contents of a directory while preserving specified items."""
+    try:
+        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
+        exclude_items = exclude_items or []
+        for item_name in os.listdir(folder_path):
+            item_path = os.path.join(folder_path, item_name)
+            if item_name in exclude_items:
+                continue
+            try:
+                if os.path.isfile(item_path) or os.path.islink(item_path):
+                    os.unlink(item_path)
+                elif os.path.isdir(item_path):
+                    shutil.rmtree(item_path)
+            except Exception as e:
+                print(f"⚠️ Error deleting {item_path}: {str(e)}")
+        return True
+    except Exception as e:
+        print(f"❌ Critical error: {str(e)}")
+        return False
+
+def clear_old_output():
+    old_output_folder = os.path.join(BASE_DIR, 'old_output')
+    try:
+        if not os.path.exists(old_output_folder):
+            return "❌ Old output folder does not exist"
+        shutil.rmtree(old_output_folder)
+        os.makedirs(old_output_folder, exist_ok=True)
+        return "✅ Old outputs successfully cleared!"
+    except Exception as e:
+        return f"🔥 Error: {str(e)}"
+
+def shorten_filename(filename, max_length=30):
+    """Shortens a filename to a specified maximum length."""
+    base, ext = os.path.splitext(filename)
+    if len(base) <= max_length:
+        return filename
+    return base[:15] + "..." + base[-10:] + ext
+
+def clean_filename(title):
+    """Removes special characters from a filename."""
+    return re.sub(r'[^\w\-_\. ]', '', title).strip()
+
+def convert_to_wav(file_path):
+    """Converts the audio file to WAV format."""
+    original_filename = os.path.basename(file_path)
+    filename, ext = os.path.splitext(original_filename)
+    if ext.lower() == '.wav':
+        return file_path
+    wav_output = os.path.join(ENSEMBLE_DIR, f"{filename}.wav")
+    try:
+        command = [
+            'ffmpeg', '-y', '-i', file_path,
+            '-acodec', 'pcm_s16le', '-ar', '44100', wav_output
+        ]
+        subprocess.run(command, check=True, capture_output=True)
+        return wav_output
+    except subprocess.CalledProcessError as e:
+        print(f"FFmpeg Error ({e.returncode}): {e.stderr.decode()}")
+        return None
+
+def generate_random_port():
+    """Generates a random port number."""
+    return random.randint(1000, 9000)
+
+def update_file_list():
+    # OUTPUT_DIR ve OLD_OUTPUT_DIR'dan .wav dosyalarını al
+    output_files = glob.glob(os.path.join(OUTPUT_DIR, "*.wav"))
+    old_output_files = glob.glob(os.path.join(OLD_OUTPUT_DIR, "*.wav"))
+    
+    # Dosya listesini birleştir
+    files = output_files + old_output_files
+    
+    # Gradio Dropdown için seçenekleri döndür
+    return gr.Dropdown(choices=files)
+
+def save_uploaded_file(uploaded_file, is_input=False, target_dir=None):
+    """Saves an uploaded file to the specified directory."""
+    media_extensions = ['.mp3', '.wav', '.flac', '.aac', '.ogg', '.m4a', '.mp4']
+    target_dir = target_dir or (INPUT_DIR if is_input else OUTPUT_DIR)
+    timestamp_patterns = [
+        r'_\d{8}_\d{6}_\d{6}$', r'_\d{14}$', r'_\d{10}$', r'_\d+$'
+    ]
+
+    if hasattr(uploaded_file, 'name'):
+        original_filename = os.path.basename(uploaded_file.name)
+    else:
+        original_filename = os.path.basename(str(uploaded_file))
+
+    if is_input:
+        base_filename = original_filename
+        for pattern in timestamp_patterns:
+            base_filename = re.sub(pattern, '', base_filename)
+        for ext in media_extensions:
+            base_filename = base_filename.replace(ext, '')
+        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
+
+    target_path = os.path.join(target_dir, clean_filename)
+    os.makedirs(target_dir, exist_ok=True)
+
+    if os.path.exists(target_path):
+        os.remove(target_path)
+
+    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
+
+def move_old_files(output_folder):
+    """Moves old files to the old_output directory."""
+    os.makedirs(OLD_OUTPUT_DIR, exist_ok=True)
+    for filename in os.listdir(output_folder):
+        file_path = os.path.join(output_folder, filename)
+        if os.path.isfile(file_path):
+            new_filename = f"{os.path.splitext(filename)[0]}_old{os.path.splitext(filename)[1]}"
+            new_file_path = os.path.join(OLD_OUTPUT_DIR, new_filename)
+            shutil.move(file_path, new_file_path)

inference.py

@@ -0,0 +1,242 @@
+# coding: utf-8
+__author__ = 'Roman Solovyev (ZFTurbo): https://github.com/ZFTurbo/'
+
+import argparse
+import time
+import librosa
+from tqdm.auto import tqdm
+import sys
+import os
+import glob
+import torch
+import soundfile as sf
+import torch.nn as nn
+from datetime import datetime
+import numpy as np
+import librosa
+
+# Using the embedded version of Python can also correctly import the utils module.
+current_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(current_dir)
+
+from utils import demix, get_model_from_config, normalize_audio, denormalize_audio
+from utils import prefer_target_instrument, apply_tta, load_start_checkpoint, load_lora_weights
+
+import warnings
+warnings.filterwarnings("ignore")
+
+
+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 get_soundfile_subtype(pcm_type, is_float=False):
+    """
+    PCM türüne göre uygun soundfile subtypei belirle
+    
+    Args:
+        pcm_type (str): PCM türü ('PCM_16', 'PCM_24', 'FLOAT')
+        is_float (bool): Float formatı kullanılıp kullanılmayacağı
+    
+    Returns:
+        str: Soundfile subtype
+    """
+    if is_float:
+        return 'FLOAT'
+    
+    subtype_map = {
+        'PCM_16': 'PCM_16',
+        'PCM_24': 'PCM_24',
+        'FLOAT': 'FLOAT'
+    }
+    return subtype_map.get(pcm_type, 'FLOAT')
+
+def run_folder(model, args, config, device, verbose: bool = False):
+    start_time = time.time()
+    model.eval()
+
+    mixture_paths = sorted(glob.glob(os.path.join(args.input_folder, '*.*')))
+    sample_rate = getattr(config.audio, 'sample_rate', 44100)
+
+    print(f"Total files found: {len(mixture_paths)}. Using sample rate: {sample_rate}")
+
+    instruments = prefer_target_instrument(config)[:]
+    os.makedirs(args.store_dir, exist_ok=True)
+
+    # Dosya sayısını ve progress için değişkenler
+    total_files = len(mixture_paths)
+    current_file = 0
+
+    # Progress tracking
+    for path in mixture_paths:
+        try:
+            # Dosya işleme başlangıcı
+            current_file += 1
+            print(f"Processing file {current_file}/{total_files}")
+            
+            mix, sr = librosa.load(path, sr=sample_rate, mono=False)
+        except Exception as e:
+            print(f'Cannot read track: {path}')
+            print(f'Error message: {str(e)}')
+            continue
+
+        mix_orig = mix.copy()
+        if 'normalize' in config.inference:
+            if config.inference['normalize'] is True:
+                mix, norm_params = normalize_audio(mix)
+
+        waveforms_orig = demix(config, model, mix, device, model_type=args.model_type)
+
+        if args.use_tta:
+            waveforms_orig = apply_tta(config, model, mix, waveforms_orig, device, args.model_type)
+
+        if args.demud_phaseremix_inst:
+            print(f"Demudding track (phase remix - instrumental): {path}")
+            instr = 'vocals' if 'vocals' in instruments else instruments[0]
+            instruments.append('instrumental_phaseremix')
+            if 'instrumental' not in instruments and 'Instrumental' not in instruments:
+                mix_modified = mix_orig - 2*waveforms_orig[instr]
+                mix_modified_ = mix_modified.copy()
+                
+                waveforms_modified = demix(config, model, mix_modified, device, model_type=args.model_type)
+                if args.use_tta:
+                    waveforms_modified = apply_tta(config, model, mix_modified, waveforms_modified, device, args.model_type)
+                
+                waveforms_orig['instrumental_phaseremix'] = mix_orig + waveforms_modified[instr]
+            else:
+                mix_modified = 2*waveforms_orig[instr] - mix_orig
+                mix_modified_ = mix_modified.copy()
+                
+                waveforms_modified = demix(config, model, mix_modified, device, model_type=args.model_type)
+                if args.use_tta:
+                    waveforms_modified = apply_tta(config, model, mix_modified, waveforms_orig, device, args.model_type)
+                
+                waveforms_orig['instrumental_phaseremix'] = mix_orig + mix_modified_ - waveforms_modified[instr]
+
+        if args.extract_instrumental:
+            instr = 'vocals' if 'vocals' in instruments else instruments[0]
+            waveforms_orig['instrumental'] = mix_orig - waveforms_orig[instr]
+            if 'instrumental' not in instruments:
+                instruments.append('instrumental')
+
+        for instr in instruments:
+            estimates = waveforms_orig[instr]
+            if 'normalize' in config.inference:
+                if config.inference['normalize'] is True:
+                    estimates = denormalize_audio(estimates, norm_params)
+
+            # Dosya formatı ve PCM türü belirleme
+            is_float = getattr(args, 'export_format', '').startswith('wav FLOAT')
+            codec = 'flac' if getattr(args, 'flac_file', False) else 'wav'
+            
+            # Subtype belirleme
+            if codec == 'flac':
+                subtype = get_soundfile_subtype(args.pcm_type, is_float)
+            else:
+                subtype = get_soundfile_subtype('FLOAT', is_float)
+
+            shortened_filename = shorten_filename(os.path.basename(path))
+            output_filename = f"{shortened_filename}_{instr}.{codec}"
+            output_path = os.path.join(args.store_dir, output_filename)
+        
+            sf.write(output_path, estimates.T, sr, subtype=subtype)
+
+        # Progress yüzdesi hesaplama
+        progress_percent = int((current_file / total_files) * 100)
+        print(f"Progress: {progress_percent}%")
+
+    print(f"Elapsed time: {time.time() - start_time:.2f} seconds.")
+
+def proc_folder(args):
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_type", type=str, default='mdx23c', 
+                        help="Model type (bandit, bs_roformer, mdx23c, etc.)")
+    parser.add_argument("--config_path", type=str, help="Path to config file")
+    parser.add_argument("--demud_phaseremix_inst", action='store_true', help="demud_phaseremix_inst")
+    parser.add_argument("--start_check_point", type=str, default='', 
+                        help="Initial checkpoint to valid weights")
+    parser.add_argument("--input_folder", type=str, help="Folder with mixtures to process")
+    parser.add_argument("--audio_path", type=str, help="Path to a single audio file to process")  # Yeni argüman
+    parser.add_argument("--store_dir", default="", type=str, help="Path to store results")
+    parser.add_argument("--device_ids", nargs='+', type=int, default=0, 
+                        help='List of GPU IDs')
+    parser.add_argument("--extract_instrumental", action='store_true', 
+                        help="Invert vocals to get instrumental if provided")
+    parser.add_argument("--force_cpu", action='store_true', 
+                        help="Force the use of CPU even if CUDA is available")
+    parser.add_argument("--flac_file", action='store_true', 
+                        help="Output flac file instead of wav")
+    parser.add_argument("--export_format", type=str, 
+                        choices=['wav FLOAT', 'flac PCM_16', 'flac PCM_24'], 
+                        default='flac PCM_24', 
+                        help="Export format and PCM type")
+    parser.add_argument("--pcm_type", type=str, 
+                        choices=['PCM_16', 'PCM_24'], 
+                        default='PCM_24', 
+                        help="PCM type for FLAC files")
+    parser.add_argument("--use_tta", action='store_true', 
+                        help="Enable test time augmentation")
+    parser.add_argument("--lora_checkpoint", type=str, default='', 
+                        help="Initial checkpoint to LoRA weights")
+
+    # Argümanları ayrıştır
+    parsed_args = parser.parse_args(args)
+
+    # Burada parsed_args.audio_path ile ses dosyası yolunu kullanabilirsiniz
+    print(f"Audio path provided: {parsed_args.audio_path}")
+    
+    if args is None:
+        args = parser.parse_args()
+    else:
+        args = parser.parse_args(args)
+
+    # Cihaz seçimi
+    device = "cpu"
+    if args.force_cpu:
+        device = "cpu"
+    elif torch.cuda.is_available():
+        print('CUDA is available, use --force_cpu to disable it.')
+        device = f'cuda:{args.device_ids[0]}' if type(args.device_ids) == list else f'cuda:{args.device_ids}'
+    elif torch.backends.mps.is_available():
+         device = "mps"
+
+    print("Using device: ", device)
+
+    model_load_start_time = time.time()
+    torch.backends.cudnn.benchmark = True
+
+    model, config = get_model_from_config(args.model_type, args.config_path)
+
+    if args.start_check_point != '':
+        load_start_checkpoint(args, model, type_='inference')
+
+    print("Instruments: {}".format(config.training.instruments))
+
+    # Çoklu CUDA GPU kullanımı
+    if type(args.device_ids) == list and len(args.device_ids) > 1 and not args.force_cpu:
+        model = nn.DataParallel(model, device_ids=args.device_ids)
+
+    model = model.to(device)
+
+    print("Model load time: {:.2f} sec".format(time.time() - model_load_start_time))
+
+    run_folder(model, args, config, device, verbose=True)
+
+
+if __name__ == "__main__":
+    proc_folder(None)

model.py

@@ -0,0 +1,714 @@
+import os
+import yaml
+from urllib.parse import quote
+from pathlib import Path
+
+# Temel dizin ve checkpoint dizini sabit olarak tanımlanıyor
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+CHECKPOINT_DIR = os.path.join(BASE_DIR, 'ckpts')
+
+def conf_edit(config_path, chunk_size, overlap):
+    """Edits the configuration file with chunk size and overlap."""
+    full_config_path = os.path.join(CHECKPOINT_DIR, os.path.basename(config_path))
+    if not os.path.exists(full_config_path):
+        raise FileNotFoundError(f"Configuration file not found: {full_config_path}")
+    
+    with open(full_config_path, 'r') as f:
+        data = yaml.load(f, Loader=yaml.SafeLoader)
+
+    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(f"Using custom overlap and chunk_size: overlap={overlap}, chunk_size={chunk_size}")
+    with open(full_config_path, 'w') as f:
+        yaml.dump(data, f, default_flow_style=False, sort_keys=False, Dumper=yaml.Dumper)
+
+def download_file(url):
+    """Downloads a file from a URL."""
+    import requests
+    encoded_url = quote(url, safe=':/')
+    path = CHECKPOINT_DIR
+    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 = requests.get(url)
+        if response.status_code == 200:
+            with open(file_path, 'wb') as f:
+                f.write(response.content)
+            print(f"File '{filename}' downloaded successfully")
+        else:
+            print(f"Error downloading '{filename}': Status code {response.status_code}")
+    except Exception as e:
+        print(f"Error downloading file '{filename}' from '{url}': {e}")
+
+# Model konfigurasyonlarını kategorize bir sözlükte tut
+MODEL_CONFIGS = {
+    "Vocal Models": {
+        'VOCALS-InstVocHQ': {
+            'model_type': 'mdx23c',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_vocals_mdx23c.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'model_vocals_mdx23c_sdr_10.17.ckpt'),
+            'download_urls': [
+                'https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/config_vocals_mdx23c.yaml',
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.0/model_vocals_mdx23c_sdr_10.17.ckpt'
+            ],
+            'needs_conf_edit': False
+        },
+        'VOCALS-MelBand-Roformer (by KimberleyJSN)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_vocals_mel_band_roformer_kj.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'MelBandRoformer.ckpt'),
+            'download_urls': [
+                'https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/KimberleyJensen/config_vocals_mel_band_roformer_kj.yaml',
+                'https://huggingface.co/KimberleyJSN/melbandroformer/resolve/main/MelBandRoformer.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'VOCALS-BS-Roformer_1297 (by viperx)': {
+            'model_type': 'bs_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'model_bs_roformer_ep_317_sdr_12.9755.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'model_bs_roformer_ep_317_sdr_12.9755.ckpt'),
+            'download_urls': [
+                'https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/main/configs/viperx/model_bs_roformer_ep_317_sdr_12.9755.yaml',
+                'https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/model_bs_roformer_ep_317_sdr_12.9755.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'VOCALS-BS-Roformer_1296 (by viperx)': {
+            'model_type': 'bs_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'model_bs_roformer_ep_368_sdr_12.9628.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'model_bs_roformer_ep_368_sdr_12.9628.ckpt'),
+            'download_urls': [
+                'https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/model_bs_roformer_ep_368_sdr_12.9628.ckpt',
+                'https://raw.githubusercontent.com/TRvlvr/application_data/main/mdx_model_data/mdx_c_configs/model_bs_roformer_ep_368_sdr_12.9628.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'VOCALS-BS-RoformerLargev1 (by unwa)': {
+            'model_type': 'bs_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_bsrofoL.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'BS-Roformer_LargeV1.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/jarredou/unwa_bs_roformer/resolve/main/BS-Roformer_LargeV1.ckpt',
+                'https://huggingface.co/jarredou/unwa_bs_roformer/raw/main/config_bsrofoL.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'VOCALS-Mel-Roformer big beta 4 (by unwa)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_melbandroformer_big_beta4.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'melband_roformer_big_beta4.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-big/resolve/main/melband_roformer_big_beta4.ckpt',
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-big/raw/main/config_melbandroformer_big_beta4.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'VOCALS-Melband-Roformer BigBeta5e (by unwa)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'big_beta5e.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'big_beta5e.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-big/resolve/main/big_beta5e.ckpt',
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-big/resolve/main/big_beta5e.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'VOCALS-VitLarge23 (by ZFTurbo)': {
+            'model_type': 'segm_models',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_vocals_segm_models.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'model_vocals_segm_models_sdr_9.77.ckpt'),
+            'download_urls': [
+                'https://raw.githubusercontent.com/ZFTurbo/Music-Source-Separation-Training/refs/heads/main/configs/config_vocals_segm_models.yaml',
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.0/model_vocals_segm_models_sdr_9.77.ckpt'
+            ],
+            'needs_conf_edit': False
+        },
+        'VOCALS-MelBand-Roformer Kim FT (by Unwa)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_kimmel_unwa_ft.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'kimmel_unwa_ft.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/kimmel_unwa_ft.ckpt',
+                'https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/config_kimmel_unwa_ft.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'VOCALS-MelBand-Roformer (by Becruily)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_instrumental_becruily.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'mel_band_roformer_vocals_becruily.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/becruily/mel-band-roformer-vocals/resolve/main/config_vocals_becruily.yaml',
+                'https://huggingface.co/becruily/mel-band-roformer-vocals/resolve/main/mel_band_roformer_vocals_becruily.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'VOCALS-Male Female-BS-RoFormer Male Female Beta 7_2889 (by aufr33)': {
+            'model_type': 'bs_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_chorus_male_female_bs_roformer.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'bs_roformer_male_female_by_aufr33_sdr_7.2889.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/RareSirMix/AIModelRehosting/resolve/main/bs_roformer_male_female_by_aufr33_sdr_7.2889.ckpt',
+                'https://huggingface.co/Sucial/Chorus_Male_Female_BS_Roformer/resolve/main/config_chorus_male_female_bs_roformer.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'VOCALS-MelBand-Roformer Kim FT 2 (by Unwa)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_kimmel_unwa_ft.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'kimmel_unwa_ft2.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/config_kimmel_unwa_ft.yaml',
+                'https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/kimmel_unwa_ft2.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'voc_gaboxBSroformer (by Gabox)': {
+            'model_type': 'bs_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'voc_gaboxBSroformer.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'voc_gaboxBSR.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/BSRoformerVocTest/resolve/main/voc_gaboxBSroformer.yaml',
+                'https://huggingface.co/GaboxR67/BSRoformerVocTest/resolve/main/voc_gaboxBSR.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'voc_gaboxMelReformer (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'voc_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'voc_gabox.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'voc_gaboxMelReformerFV1 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'voc_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'voc_gaboxFv1.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gaboxFv1.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'voc_gaboxMelReformerFV2 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'voc_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'voc_gaboxFv2.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gaboxFv2.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'VOCALS-MelBand-Roformer Kim FT 2 Blendless (by unwa)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_kimmel_unwa_ft.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'kimmel_unwa_ft2_bleedless.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/config_kimmel_unwa_ft.yaml',
+                'https://huggingface.co/pcunwa/Kim-Mel-Band-Roformer-FT/resolve/main/kimmel_unwa_ft2_bleedless.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'Voc_Fv3 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'voc_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'voc_Fv3.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/vocals/voc_Fv3.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'FullnessVocalModel (by Amane)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'FullnessVocalModel.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/Aname-Tommy/MelBandRoformers/blob/main/config.yaml',
+                'https://huggingface.co/Aname-Tommy/MelBandRoformers/blob/main/FullnessVocalModel.ckpt'
+            ],
+            'needs_conf_edit': True
+        }
+    },
+    "Instrumental Models": {
+        'INST-Mel-Roformer v1 (by unwa)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_melbandroformer_inst.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'melband_roformer_inst_v1.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/melband_roformer_inst_v1.ckpt',
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/raw/main/config_melbandroformer_inst.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'INST-Mel-Roformer v2 (by unwa)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_melbandroformer_inst_v2.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'melband_roformer_inst_v2.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/melband_roformer_inst_v2.ckpt',
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/raw/main/config_melbandroformer_inst_v2.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'INST-VOC-Mel-Roformer a.k.a. duality (by unwa)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_melbandroformer_instvoc_duality.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'melband_roformer_instvoc_duality_v1.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/resolve/main/melband_roformer_instvoc_duality_v1.ckpt',
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/raw/main/config_melbandroformer_instvoc_duality.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'INST-VOC-Mel-Roformer a.k.a. duality v2 (by unwa)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_melbandroformer_instvoc_duality.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'melband_roformer_instvox_duality_v2.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/resolve/main/melband_roformer_instvox_duality_v2.ckpt',
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-InstVoc-Duality/raw/main/config_melbandroformer_instvoc_duality.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'INST-MelBand-Roformer (by Becruily)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_instrumental_becruily.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'mel_band_roformer_instrumental_becruily.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/becruily/mel-band-roformer-instrumental/resolve/main/config_instrumental_becruily.yaml',
+                'https://huggingface.co/becruily/mel-band-roformer-instrumental/resolve/main/mel_band_roformer_instrumental_becruily.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'inst_v1e (by unwa)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_melbandroformer_inst.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'inst_v1e.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/inst_v1e.ckpt',
+                'https://huggingface.co/pcunwa/Mel-Band-Roformer-Inst/resolve/main/config_melbandroformer_inst.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'inst_gabox (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'inst_gabox.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'inst_gaboxBV1 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'inst_gaboxBv1.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxBv1.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'inst_gaboxBV2 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'inst_gaboxBv2.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxBv2.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'inst_gaboxBFV1 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'gaboxFv1.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv1.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'inst_gaboxFV2 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'inst_gaboxFv2.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv2.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'inst_Fv3 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'inst_gaboxFv3.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv3.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'Intrumental_Gabox (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'intrumental_gabox.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/intrumental_gabox.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'inst_Fv4Noise (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'inst_Fv4Noise.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_Fv4Noise.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'INSTV5 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'INSTV5.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV5.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'inst_gaboxFV1 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'inst_gaboxFv1.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gaboxFv1.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'inst_gaboxFV6 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'INSTV6.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV6.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'INSTV5N (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'INSTV5N.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV5N.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'INSTV6N (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'INSTV6N.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/INSTV6N.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'Inst_GaboxV7 (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'inst_gabox.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'Inst_GaboxV7.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/inst_gabox.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/Inst_GaboxV7.ckpt'
+            ],
+            'needs_conf_edit': True
+        }
+    },
+    "4-Stem Models": {
+        '4STEMS-SCNet_MUSDB18 (by starrytong)': {
+            'model_type': 'scnet',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_musdb18_scnet.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'scnet_checkpoint_musdb18.ckpt'),
+            'download_urls': [
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.6/config_musdb18_scnet.yaml',
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.6/scnet_checkpoint_musdb18.ckpt'
+            ],
+            'needs_conf_edit': False
+        },
+        '4STEMS-SCNet_XL_MUSDB18 (by ZFTurbo)': {
+            'model_type': 'scnet',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_musdb18_scnet_xl.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'model_scnet_ep_54_sdr_9.8051.ckpt'),
+            'download_urls': [
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.13/config_musdb18_scnet_xl.yaml',
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.13/model_scnet_ep_54_sdr_9.8051.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        '4STEMS-SCNet_Large (by starrytong)': {
+            'model_type': 'scnet',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_musdb18_scnet_large_starrytong.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'SCNet-large_starrytong_fixed.ckpt'),
+            'download_urls': [
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.9/config_musdb18_scnet_large_starrytong.yaml',
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.9/SCNet-large_starrytong_fixed.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        '4STEMS-BS-Roformer_MUSDB18 (by ZFTurbo)': {
+            'model_type': 'bs_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_bs_roformer_384_8_2_485100.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'model_bs_roformer_ep_17_sdr_9.6568.ckpt'),
+            'download_urls': [
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.12/config_bs_roformer_384_8_2_485100.yaml',
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v1.0.12/model_bs_roformer_ep_17_sdr_9.6568.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'MelBandRoformer4StemFTLarge (SYH99999)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'MelBandRoformer4StemFTLarge.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/SYH99999/MelBandRoformer4StemFTLarge/resolve/main/config.yaml',
+                'https://huggingface.co/SYH99999/MelBandRoformer4StemFTLarge/resolve/main/MelBandRoformer4StemFTLarge.ckpt'
+            ],
+            'needs_conf_edit': True
+        }
+    },
+    "Denoise Models": {
+        'DENOISE-MelBand-Roformer-1 (by aufr33)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'model_mel_band_roformer_denoise.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt',
+                'https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/model_mel_band_roformer_denoise.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'DENOISE-MelBand-Roformer-2 (by aufr33)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'model_mel_band_roformer_denoise.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt',
+                'https://huggingface.co/jarredou/aufr33_MelBand_Denoise/resolve/main/model_mel_band_roformer_denoise.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'denoisedebleed (by Gabox)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'model_mel_band_roformer_denoise.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'denoisedebleed.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/poiqazwsx/melband-roformer-denoise/resolve/main/model_mel_band_roformer_denoise.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/resolve/main/melbandroformers/instrumental/denoisedebleed.ckpt'
+            ],
+            'needs_conf_edit': True
+        }
+    },
+    "Dereverb Models": {
+        'DE-REVERB-MDX23C (by aufr33 & jarredou)': {
+            'model_type': 'mdx23c',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_dereverb_mdx23c.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'dereverb_mdx23c_sdr_6.9096.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/jarredou/aufr33_jarredou_MDXv3_DeReverb/resolve/main/dereverb_mdx23c_sdr_6.9096.ckpt',
+                'https://huggingface.co/jarredou/aufr33_jarredou_MDXv3_DeReverb/resolve/main/config_dereverb_mdx23c.yaml'
+            ],
+            'needs_conf_edit': False
+        },
+        'DE-REVERB-MelBand-Roformer aggr./v2/19.1729 (by anvuew)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'dereverb_mel_band_roformer_anvuew.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt',
+                'https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'DE-REVERB-Echo-MelBand-Roformer (by Sucial)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_dereverb-echo_mel_band_roformer.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/Sucial/Dereverb-Echo_Mel_Band_Roformer/resolve/main/dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt',
+                'https://huggingface.co/Sucial/Dereverb-Echo_Mel_Band_Roformer/resolve/main/config_dereverb-echo_mel_band_roformer.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'dereverb_mel_band_roformer_less_aggressive_anvuew': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'dereverb_mel_band_roformer_anvuew.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml',
+                'https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'dereverb_mel_band_roformer_anvuew': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'dereverb_mel_band_roformer_anvuew.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml',
+                'https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'dereverb_mel_band_roformer_mono (by anvuew)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'dereverb_mel_band_roformer_anvuew.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'dereverb_mel_band_roformer_mono_anvuew_sdr_20.4029.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_anvuew.yaml',
+                'https://huggingface.co/anvuew/dereverb_mel_band_roformer/resolve/main/dereverb_mel_band_roformer_mono_anvuew_sdr_20.4029.ckpt'
+            ],
+            'needs_conf_edit': True
+        }
+    },
+    "Other Models": {
+        'KARAOKE-MelBand-Roformer (by aufr33 & viperx)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_mel_band_roformer_karaoke.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/jarredou/aufr33-viperx-karaoke-melroformer-model/resolve/main/mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt',
+                'https://huggingface.co/jarredou/aufr33-viperx-karaoke-melroformer-model/resolve/main/config_mel_band_roformer_karaoke.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'OTHER-BS-Roformer_1053 (by viperx)': {
+            'model_type': 'bs_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'model_bs_roformer_ep_937_sdr_10.5309.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'model_bs_roformer_ep_937_sdr_10.5309.ckpt'),
+            'download_urls': [
+                'https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/model_bs_roformer_ep_937_sdr_10.5309.ckpt',
+                'https://raw.githubusercontent.com/TRvlvr/application_data/main/mdx_model_data/mdx_c_configs/model_bs_roformer_ep_937_sdr_10.5309.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'CROWD-REMOVAL-MelBand-Roformer (by aufr33)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'model_mel_band_roformer_crowd.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt'),
+            'download_urls': [
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.4/mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt',
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.4/model_mel_band_roformer_crowd.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'CINEMATIC-BandIt_Plus (by kwatcharasupat)': {
+            'model_type': 'bandit',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_dnr_bandit_bsrnn_multi_mus64.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'model_bandit_plus_dnr_sdr_11.47.chpt'),
+            'download_urls': [
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.3/config_dnr_bandit_bsrnn_multi_mus64.yaml',
+                'https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/download/v.1.0.3/model_bandit_plus_dnr_sdr_11.47.chpt'
+            ],
+            'needs_conf_edit': False
+        },
+        'DRUMSEP-MDX23C_DrumSep_6stem (by aufr33 & jarredou)': {
+            'model_type': 'mdx23c',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'aufr33-jarredou_DrumSep_model_mdx23c_ep_141_sdr_10.8059.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'aufr33-jarredou_DrumSep_model_mdx23c_ep_141_sdr_10.8059.ckpt'),
+            'download_urls': [
+                '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',
+                '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'
+            ],
+            'needs_conf_edit': False
+        },
+        'bleed_suppressor_v1 (by unwa)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_bleed_suppressor_v1.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'bleed_suppressor_v1.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/ASesYusuf1/MODELS/resolve/main/bleed_suppressor_v1.ckpt',
+                'https://huggingface.co/ASesYusuf1/MODELS/resolve/main/config_bleed_suppressor_v1.yaml'
+            ],
+            'needs_conf_edit': True
+        },
+        'SYH99999/MelBandRoformerSYHFTB1_Model1 (by Amane)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'model.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/config.yaml',
+                'https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/model.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'SYH99999/MelBandRoformerSYHFTB1_Model2 (by Amane)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'model2.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/config.yaml',
+                'https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/model2.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'SYH99999/MelBandRoformerSYHFTB1_Model3 (by Amane)': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'model3.ckpt'),
+            'download_urls': [
+                'https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/config.yaml',
+                'https://huggingface.co/SYH99999/MelBandRoformerSYHFTB1/resolve/main/model3.ckpt'
+            ],
+            'needs_conf_edit': True
+        },
+        'KaraokeGabox': {
+            'model_type': 'mel_band_roformer',
+            'config_path': os.path.join(CHECKPOINT_DIR, 'config_mel_band_roformer_karaoke.yaml'),
+            'start_check_point': os.path.join(CHECKPOINT_DIR, 'KaraokeGabox.ckpt'),
+            'download_urls': [
+                'https://github.com/deton24/Colab-for-new-MDX_UVR_models/releases/download/v1.0.0/config_mel_band_roformer_karaoke.yaml',
+                'https://huggingface.co/GaboxR67/MelBandRoformers/blob/main/melbandroformers/experimental/KaraokeGabox.ckpt'
+            ],
+            'needs_conf_edit': True
+        }
+    }
+}
+
+def get_model_config(clean_model=None, chunk_size=None, overlap=None):
+    """Returns model type, config path, and checkpoint path for a given model name, downloading files if needed."""
+    if clean_model is None:
+        return {model_name for category in MODEL_CONFIGS.values() for model_name in category.keys()}
+    
+    for category in MODEL_CONFIGS.values():
+        if clean_model in category:
+            config = category[clean_model]
+            for url in config['download_urls']:
+                download_file(url)
+            if config['needs_conf_edit'] and chunk_size is not None and overlap is not None:
+                conf_edit(config['config_path'], chunk_size, overlap)
+            return config['model_type'], config['config_path'], config['start_check_point']
+    return "", "", ""
+
+get_model_config.keys = lambda: {model_name for category in MODEL_CONFIGS.values() for model_name in category.keys()}

processing.py

@@ -0,0 +1,320 @@
+import os
+import glob
+import subprocess
+import time
+import gc
+import shutil
+import sys
+current_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(current_dir)
+
+from datetime import datetime
+from helpers import INPUT_DIR, OLD_OUTPUT_DIR, ENSEMBLE_DIR, AUTO_ENSEMBLE_TEMP, move_old_files, clear_directory, BASE_DIR
+from model import get_model_config
+import torch
+import yaml
+import gradio as gr
+import threading
+import random
+import librosa
+import soundfile as sf
+import numpy as np
+import requests
+import json
+import locale
+import re
+import psutil
+import concurrent.futures
+from tqdm import tqdm
+from google.oauth2.credentials import Credentials
+import tempfile
+from urllib.parse import urlparse, quote
+import gdown
+from clean_model import clean_model_name, shorten_filename, clean_filename
+
+import warnings
+warnings.filterwarnings("ignore")
+
+# BASE_DIR'i dinamik olarak güncel dizine ayarla
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))  # processing.py'nin bulunduğu dizin
+INFERENCE_PATH = os.path.join(BASE_DIR, "inference.py")  # inference.py'nin tam yolu
+OUTPUT_DIR = os.path.join(BASE_DIR, "output")  # Çıkış dizini BASE_DIR/output olarak güncellendi
+AUTO_ENSEMBLE_OUTPUT = os.path.join(BASE_DIR, "ensemble_output")  # Ensemble çıkış dizini
+
+def extract_model_name(full_model_string):
+    """Extracts the clean model name from a string."""
+    if not full_model_string:
+        return ""
+    cleaned = str(full_model_string)
+    if ' - ' in cleaned:
+        cleaned = cleaned.split(' - ')[0]
+    emoji_prefixes = ['✅ ', '👥 ', '🗣️ ', '🏛️ ', '🔇 ', '🔉 ', '🎬 ', '🎼 ', '✅(?) ']
+    for prefix in emoji_prefixes:
+        if cleaned.startswith(prefix):
+            cleaned = cleaned[len(prefix):]
+    return cleaned.strip()
+
+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:
+        # inference.py'nin tam yolunu kullan
+        cmd_parts = [
+            "python", INFERENCE_PATH,
+            "--model_type", model_type,
+            "--config_path", config_path,
+            "--start_check_point", start_check_point,
+            "--input_folder", INPUT_DIR,
+            "--store_dir", OUTPUT_DIR,
+        ]
+        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")
+
+        process = subprocess.Popen(
+            cmd_parts,
+            cwd=BASE_DIR,  # Çalışma dizini olarak BASE_DIR kullan
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+            bufsize=1,
+            universal_newlines=True
+        )
+
+        for line in process.stdout:
+            print(line.strip())
+        for line in process.stderr:
+            print(line.strip())
+
+        process.wait()
+
+        filename_model = clean_model_name(clean_model)
+
+        def rename_files_with_model(folder, filename_model):
+            for filename in sorted(os.listdir(folder)):
+                file_path = os.path.join(folder, filename)
+                if not any(filename.lower().endswith(ext) for ext in ['.mp3', '.wav', '.flac', '.aac', '.ogg', '.m4a']):
+                    continue
+                base, ext = os.path.splitext(filename)
+                clean_base = base.strip('_- ')
+                new_filename = f"{clean_base}_{filename_model}{ext}"
+                new_file_path = os.path.join(folder, new_filename)
+                os.rename(file_path, new_file_path)
+
+        rename_files_with_model(OUTPUT_DIR, filename_model)
+
+        output_files = os.listdir(OUTPUT_DIR)
+
+        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
+
+        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')
+
+        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
+
+        clear_directory(INPUT_DIR)
+
+def process_audio(input_audio_file, model, chunk_size, overlap, export_format, use_tta, demud_phaseremix_inst, extract_instrumental, clean_model, *args, **kwargs):
+    """Processes audio using the specified model and returns separated stems."""
+    if input_audio_file is not None:
+        audio_path = input_audio_file.name
+    else:
+        existing_files = os.listdir(INPUT_DIR)
+        if existing_files:
+            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
+
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+    os.makedirs(OLD_OUTPUT_DIR, exist_ok=True)
+    move_old_files(OUTPUT_DIR)
+
+    clean_model_name_full = extract_model_name(model)
+    print(f"Processing audio from: {audio_path} using model: {clean_model_name_full}")
+
+    model_type, config_path, start_check_point = get_model_config(clean_model_name_full, chunk_size, overlap)
+
+    outputs = run_command_and_process_files(
+        model_type=model_type,
+        config_path=config_path,
+        start_check_point=start_check_point,
+        INPUT_DIR=INPUT_DIR,
+        OUTPUT_DIR=OUTPUT_DIR,
+        extract_instrumental=extract_instrumental,
+        use_tta=use_tta,
+        demud_phaseremix_inst=demud_phaseremix_inst,
+        clean_model=clean_model_name_full
+    )
+
+    return outputs
+
+def ensemble_audio_fn(files, method, weights):
+    try:
+        if len(files) < 2:
+            return None, "⚠️ Minimum 2 files required"
+        
+        valid_files = [f for f in files if os.path.exists(f)]
+        if len(valid_files) < 2:
+            return None, "❌ Valid files not found"
+        
+        output_dir = os.path.join(BASE_DIR, "ensembles")  # BASE_DIR üzerinden dinamik
+        os.makedirs(output_dir, exist_ok=True)
+        
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        output_path = f"{output_dir}/ensemble_{timestamp}.wav"
+        
+        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)}"
+
+def auto_ensemble_process(input_audio_file, selected_models, chunk_size, overlap, export_format, use_tta, extract_instrumental, ensemble_type, _state, *args, **kwargs):
+    """Processes audio with multiple models and performs ensemble."""
+    try:
+        if not selected_models or len(selected_models) < 1:
+            return None, "❌ No models selected"
+
+        if input_audio_file is None:
+            existing_files = os.listdir(INPUT_DIR)
+            if not existing_files:
+                return None, "❌ No input audio provided"
+            audio_path = os.path.join(INPUT_DIR, existing_files[0])
+        else:
+            audio_path = input_audio_file.name
+
+        # AUTO_ENSEMBLE_TEMP'i de BASE_DIR üzerinden tanımla
+        auto_ensemble_temp = os.path.join(BASE_DIR, "auto_ensemble_temp")
+        os.makedirs(auto_ensemble_temp, exist_ok=True)
+        os.makedirs(AUTO_ENSEMBLE_OUTPUT, exist_ok=True)
+        clear_directory(auto_ensemble_temp)
+
+        all_outputs = []
+        for model in selected_models:
+            clean_model = extract_model_name(model)
+            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 = get_model_config(clean_model, chunk_size, overlap)
+
+            cmd = [
+                "python", INFERENCE_PATH,
+                "--model_type", model_type,
+                "--config_path", config_path,
+                "--start_check_point", start_check_point,
+                "--input_folder", INPUT_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)}")
+            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)}"
+
+            model_outputs = glob.glob(os.path.join(model_output_dir, "*.wav"))
+            if not model_outputs:
+                raise FileNotFoundError(f"{model} failed to produce output")
+            all_outputs.extend(model_outputs)
+
+        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(all_outputs)
+
+        quoted_files = [f'"{f}"' for f in all_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}"'
+        ]
+
+        result = subprocess.run(
+            " ".join(ensemble_cmd),
+            shell=True,
+            capture_output=True,
+            text=True,
+            check=True
+        )
+
+        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:
+        shutil.rmtree(auto_ensemble_temp, ignore_errors=True)
+        gc.collect()

requirements.txt

@@ -0,0 +1,35 @@
+pip
+setuptools
+wheel
+numpy
+pandas
+scipy
+torch
+torchvision
+torchmetrics
+ml_collections
+segmentation_models_pytorch
+einops
+matplotlib
+librosa
+soundfile
+demucs
+audiomentations
+torch_audiomentations
+httpx
+gradio
+google-api-python-client
+validators
+pytube
+tqdm
+psutil
+omegaconf
+beartype
+asteroid
+torchseg
+yt_dlp
+rotary_embedding_torch
+noisereduce
+loralib
+hyper_connections==0.1.11
+pyngrok