Update app.py

app.py

@@ -1,18 +1,14 @@
 import os
 import gradio as gr
-import spaces
 from infer_rvc_python import BaseLoader
 import random
 import logging
 import time
-import soundfile as sf
-from infer_rvc_python.main import download_manager, load_hu_bert, Config
 import zipfile
 import edge_tts
 import asyncio
 import librosa
 import traceback
-import soundfile as sf
 from pedalboard import Pedalboard, Reverb, Compressor, HighpassFilter
 from pedalboard.io import AudioFile
 from pydub import AudioSegment
@@ -23,19 +19,11 @@ import shutil
 import threading
 import argparse
 import sys
+import gc  # Garbage Collection برای مدیریت حافظه
 
-parser = argparse.ArgumentParser(description="Run the app with optional sharing")
-parser.add_argument(
-    '--share',
-    action='store_true',
-    help='Enable sharing mode'
-)
-parser.add_argument(
-    '--theme',
-    type=str,
-    default="aliabid94/new-theme",
-    help='Set the theme (default: aliabid94/new-theme)'
-)
+parser = argparse.ArgumentParser()
+parser.add_argument('--share', action='store_true')
+parser.add_argument('--theme', type=str, default="aliabid94/new-theme")
 args = parser.parse_args()
 
 IS_COLAB = True if ('google.colab' in sys.modules or args.share) else False
@@ -43,314 +31,100 @@ IS_ZERO_GPU = os.getenv("SPACES_ZERO_GPU")
 
 logging.getLogger("infer_rvc_python").setLevel(logging.ERROR)
 
-converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None)
-converter.hu_bert_model = load_hu_bert(Config(only_cpu=False), converter.hubert_path)
+# تنظیم لودر (برای CPU)
+converter = BaseLoader(only_cpu=True, hubert_path=None, rmvpe_path=None)
+
+# --- توابع کمکی ---
+def download_manager(url, path, extension="", overwrite=False, progress=True):
+    from infer_rvc_python.main import download_manager as dm
+    return dm(url, path, extension, overwrite, progress)
 
 test_model = "https://huggingface.co/sail-rvc/Aldeano_Minecraft__RVC_V2_-_500_Epochs_/resolve/main/model.pth?download=true, https://huggingface.co/sail-rvc/Aldeano_Minecraft__RVC_V2_-_500_Epochs_/resolve/main/model.index?download=true"
 test_names = ["model.pth", "model.index"]
 
 for url, filename in zip(test_model.split(", "), test_names):
     try:
-        download_manager(
-            url=url,
-            path=".",
-            extension="",
-            overwrite=False,
-            progress=True,
-        )
-        if not os.path.isfile(filename):
-            raise FileNotFoundError
+        download_manager(url=url, path=".", extension="", overwrite=False, progress=True)
     except Exception:
-        with open(filename, "wb") as f:
-            pass
+        pass
 
-title = "<center><strong><font size='7'>RVC⚡ZERO</font></strong></center>"
-description = "This demo is provided for educational and research purposes only. The authors and contributors of this project do not endorse or encourage any misuse or unethical use of this software. Any use of this software for purposes other than those intended is solely at the user's own risk. The authors and contributors shall not be held responsible for any damages or liabilities arising from the use of this demo inappropriately." if IS_ZERO_GPU else ""
-RESOURCES = "- You can also try `RVC⚡ZERO` in Colab’s free tier, which provides free GPU [link](https://github.com/R3gm/rvc_zero_ui?tab=readme-ov-file#rvczero)."
+title = "<center><strong><font size='7'>RVC⚡ZERO (Worker)</font></strong></center>"
+description = "Worker Node - Optimized for CPU"
 theme = args.theme
-delete_cache_time = (3200, 3200) if IS_ZERO_GPU else (86400, 86400)
-
-PITCH_ALGO_OPT = [
-    "pm",
-    "harvest",
-    "crepe",
-    "rmvpe",
-    "rmvpe+",
-]
-
 
 async def get_voices_list(proxy=None):
-    """Print all available voices."""
     from edge_tts import list_voices
     voices = await list_voices(proxy=proxy)
     voices = sorted(voices, key=lambda voice: voice["ShortName"])
-
-    table = [
-        {
-            "ShortName": voice["ShortName"],
-            "Gender": voice["Gender"],
-            "ContentCategories": ", ".join(voice["VoiceTag"]["ContentCategories"]),
-            "VoicePersonalities": ", ".join(voice["VoiceTag"]["VoicePersonalities"]),
-            "FriendlyName": voice["FriendlyName"],
-        }
-        for voice in voices
-    ]
-
-    return table
-
+    return [{"ShortName": v["ShortName"], "Gender": v["Gender"], "FriendlyName": v["FriendlyName"]} for v in voices]
 
 def find_files(directory):
     file_paths = []
-    for filename in os.listdir(directory):
-        # Check if the file has the desired extension
-        if filename.endswith('.pth') or filename.endswith('.zip') or filename.endswith('.index'):
-            # If yes, add the file path to the list
-            file_paths.append(os.path.join(directory, filename))
-
+    if os.path.exists(directory):
+        for filename in os.listdir(directory):
+            if filename.endswith('.pth') or filename.endswith('.zip') or filename.endswith('.index'):
+                file_paths.append(os.path.join(directory, filename))
     return file_paths
 
-
 def unzip_in_folder(my_zip, my_dir):
     with zipfile.ZipFile(my_zip) as zip:
         for zip_info in zip.infolist():
-            if zip_info.is_dir():
-                continue
+            if zip_info.is_dir(): continue
             zip_info.filename = os.path.basename(zip_info.filename)
             zip.extract(zip_info, my_dir)
 
-
 def find_my_model(a_, b_):
-
-    if a_ is None or a_.endswith(".pth"):
-        return a_, b_
-
-    txt_files = []
-    for base_file in [a_, b_]:
-        if base_file is not None and base_file.endswith(".txt"):
-            txt_files.append(base_file)
-
-    directory = os.path.dirname(a_)
-
-    for txt in txt_files:
-        with open(txt, 'r') as file:
-            first_line = file.readline()
-
-        download_manager(
-            url=first_line.strip(),
-            path=directory,
-            extension="",
-        )
-
-    for f in find_files(directory):
-        if f.endswith(".zip"):
-            unzip_in_folder(f, directory)
-
-    model = None
-    index = None
-    end_files = find_files(directory)
-
-    for ff in end_files:
-        if ff.endswith(".pth"):
-            model = os.path.join(directory, ff)
-            gr.Info(f"Model found: {ff}")
-        if ff.endswith(".index"):
-            index = os.path.join(directory, ff)
-            gr.Info(f"Index found: {ff}")
-
-    if not model:
-        gr.Error(f"Model not found in: {end_files}")
-
-    if not index:
-        gr.Warning("Index not found")
-
-    return model, index
-
-
-def ensure_valid_file(url):
-    if "huggingface" not in url:
-        raise ValueError("Only downloads from Hugging Face are allowed")
-
-    try:
-        request = urllib.request.Request(url, method="HEAD")
-        with urllib.request.urlopen(request) as response:
-            content_length = response.headers.get("Content-Length")
-
-        if content_length is None:
-            raise ValueError("No Content-Length header found")
-
-        file_size = int(content_length)
-        # print("debug", url, file_size)
-        if file_size > 900000000 and IS_ZERO_GPU:
-            raise ValueError("The file is too large. Max allowed is 900 MB.")
-
-        return file_size
-
-    except Exception as e:
-        raise e
-
-
-def clear_files(directory):
-    time.sleep(15)
-    print(f"Clearing files: {directory}.")
-    shutil.rmtree(directory)
-
-
-def get_my_model(url_data, progress=gr.Progress(track_tqdm=True)):
-
-    if not url_data:
-        return None, None
-
-    if "," in url_data:
-        a_, b_ = url_data.split(",")
-        a_, b_ = a_.strip().replace("/blob/", "/resolve/"), b_.strip().replace("/blob/", "/resolve/")
-    else:
-        a_, b_ = url_data.strip().replace("/blob/", "/resolve/"), None
-
-    out_dir = "downloads"
-    folder_download = str(random.randint(1000, 9999))
-    directory = os.path.join(out_dir, folder_download)
-    os.makedirs(directory, exist_ok=True)
-
-    try:
-        valid_url = [a_] if not b_ else [a_, b_]
-        for link in valid_url:
-            ensure_valid_file(link)
-            download_manager(
-                url=link,
-                path=directory,
-                extension="",
-            )
-
-        for f in find_files(directory):
-            if f.endswith(".zip"):
-                unzip_in_folder(f, directory)
-
-        model = None
-        index = None
-        end_files = find_files(directory)
-
-        for ff in end_files:
-            if ff.endswith(".pth"):
-                model = ff
-                gr.Info(f"Model found: {ff}")
-            if ff.endswith(".index"):
-                index = ff
-                gr.Info(f"Index found: {ff}")
-
-        if not model:
-            raise ValueError(f"Model not found in: {end_files}")
-
-        if not index:
-            gr.Warning("Index not found")
-        else:
-            index = os.path.abspath(index)
-
-        return os.path.abspath(model), index
-
-    except Exception as e:
-        raise e
-    finally:
-        # time.sleep(10)
-        # shutil.rmtree(directory)
-        t = threading.Thread(target=clear_files, args=(directory,))
-        t.start()
-
+    if a_ is None or a_.endswith(".pth"): return a_, b_
+    return a_, b_
 
 def add_audio_effects(audio_list, type_output):
-    print("Audio effects")
-
     result = []
     for audio_path in audio_list:
         try:
             output_path = f'{os.path.splitext(audio_path)[0]}_effects.{type_output}'
-
-            # Initialize audio effects plugins
-            board = Pedalboard(
-                [
-                    HighpassFilter(),
-                    Compressor(ratio=4, threshold_db=-15),
-                    Reverb(room_size=0.10, dry_level=0.8, wet_level=0.2, damping=0.7)
-                 ]
-            )
-
-            # Temporary WAV to hold processed data before exporting
+            board = Pedalboard([HighpassFilter(), Compressor(ratio=4, threshold_db=-15), Reverb(room_size=0.10, dry_level=0.8, wet_level=0.2, damping=0.7)])
             temp_wav = f'{os.path.splitext(audio_path)[0]}_temp.wav'
-
             with AudioFile(audio_path) as f:
                 with AudioFile(temp_wav, 'w', f.samplerate, f.num_channels) as o:
                     while f.tell() < f.frames:
                         chunk = f.read(int(f.samplerate))
                         effected = board(chunk, f.samplerate, reset=False)
                         o.write(effected)
-
-            # Convert with pydub to desired output type
             audio_seg = AudioSegment.from_file(temp_wav, format=type_output)
-            audio_seg.export(output_path, format=type_output, bitrate=("320k" if type_output == "mp3" else None))
-
-            # Clean up temp file
+            audio_seg.export(output_path, format=type_output, bitrate="320k" if type_output == "mp3" else None)
             os.remove(temp_wav)
-
             result.append(output_path)
-        except Exception as e:
-            traceback.print_exc()
-            print(f"Error noisereduce: {str(e)}")
+        except Exception:
             result.append(audio_path)
-
     return result
 
-
 def apply_noisereduce(audio_list, type_output):
-    # https://github.com/sa-if/Audio-Denoiser
-    print("Noice reduce")
-
     result = []
     for audio_path in audio_list:
         out_path = f"{os.path.splitext(audio_path)[0]}_noisereduce.{type_output}"
-
         try:
-            # Load audio file
             audio = AudioSegment.from_file(audio_path)
-
-            # Convert audio to numpy array
             samples = np.array(audio.get_array_of_samples())
-
-            # Reduce noise
             reduced_noise = nr.reduce_noise(samples, sr=audio.frame_rate, prop_decrease=0.6)
-
-            # Convert reduced noise signal back to audio
-            reduced_audio = AudioSegment(
-                reduced_noise.tobytes(), 
-                frame_rate=audio.frame_rate, 
-                sample_width=audio.sample_width,
-                channels=audio.channels
-            )
-
-            # Save reduced audio to file
-            reduced_audio.export(out_path, format=type_output, bitrate=("320k" if type_output == "mp3" else None))
+            reduced_audio = AudioSegment(reduced_noise.tobytes(), frame_rate=audio.frame_rate, sample_width=audio.sample_width, channels=audio.channels)
+            reduced_audio.export(out_path, format=type_output, bitrate="320k" if type_output == "mp3" else None)
             result.append(out_path)
-
-        except Exception as e:
-            traceback.print_exc()
-            print(f"Error noisereduce: {str(e)}")
+        except Exception:
             result.append(audio_path)
-
     return result
 
-
-@spaces.GPU()
 def convert_now(audio_files, random_tag, converter, type_output, steps):
+    # اجبار به اجرای متوالی و تک‌رشته‌ای برای جلوگیری از خطای رم و پردازش در CPU
     for step in range(steps):
         audio_files = converter(
             audio_files,
             random_tag,
             overwrite=False,
-            parallel_workers=(2 if IS_COLAB else 8),
+            parallel_workers=1, # مهم برای CPU: فقط ۱
             type_output=type_output,
         )
-
     return audio_files
 
-
 def run(
     audio_files,
     file_m,
@@ -366,24 +140,18 @@ def run(
     type_output,
     steps,
 ):
-    if not audio_files:
-        raise ValueError("The audio pls")
-
-    if isinstance(audio_files, str):
-        audio_files = [audio_files]
-
-    try:
-        duration_base = librosa.get_duration(filename=audio_files[0])
-        print("Duration:", duration_base)
-    except Exception as e:
-        print(e)
+    # پاکسازی حافظه قبل از شروع
+    gc.collect()
+    
+    if not audio_files: raise ValueError("No audio files provided")
+    if isinstance(audio_files, str): audio_files = [audio_files]
 
     if file_m is not None and file_m.endswith(".txt"):
         file_m, file_index = find_my_model(file_m, file_index)
-        print(file_m, file_index)
 
     random_tag = "USER_"+str(random.randint(10000000, 99999999))
 
+    # بارگذاری مدل
     converter.apply_conf(
         tag=random_tag,
         file_model=file_m,
@@ -398,461 +166,109 @@ def run(
     )
     time.sleep(0.1)
 
+    # شروع پردازش
     result = convert_now(audio_files, random_tag, converter, type_output, steps)
 
     if active_noise_reduce:
         result = apply_noisereduce(result, type_output)
-
     if audio_effects:
         result = add_audio_effects(result, type_output)
-
+        
+    # پاکسازی نهایی حافظه
+    gc.collect()
     return result
 
-
-def audio_conf():
-    return gr.File(
-        label="Audio files",
-        file_count="multiple",
-        type="filepath",
-        container=True,
-    )
-
-
-def model_conf():
-    return gr.File(
-        label="Model file",
-        type="filepath",
-        height=130,
-    )
-
-
-def pitch_algo_conf():
-    return gr.Dropdown(
-        PITCH_ALGO_OPT,
-        value=PITCH_ALGO_OPT[4],
-        label="Pitch algorithm",
-        visible=True,
-        interactive=True,
-    )
-
-
-def pitch_lvl_conf():
-    return gr.Slider(
-        label="Pitch level",
-        minimum=-24,
-        maximum=24,
-        step=1,
-        value=0,
-        visible=True,
-        interactive=True,
-    )
-
-
-def index_conf():
-    return gr.File(
-        label="Index file",
-        type="filepath",
-        height=130,
-    )
-
-
-def index_inf_conf():
-    return gr.Slider(
-        minimum=0,
-        maximum=1,
-        label="Index influence",
-        value=0.75,
-    )
-
-
-def respiration_filter_conf():
-    return gr.Slider(
-        minimum=0,
-        maximum=7,
-        label="Respiration median filtering",
-        value=3,
-        step=1,
-        interactive=True,
-    )
-
-
-def envelope_ratio_conf():
-    return gr.Slider(
-        minimum=0,
-        maximum=1,
-        label="Envelope ratio",
-        value=0.25,
-        interactive=True,
-    )
-
-
-def consonant_protec_conf():
-    return gr.Slider(
-        minimum=0,
-        maximum=0.5,
-        label="Consonant breath protection",
-        value=0.5,
-        interactive=True,
-    )
-
-
-def button_conf():
-    return gr.Button(
-        "Inference",
-        variant="primary",
-    )
-
-
-def output_conf():
-    return gr.File(
-        label="Result",
-        file_count="multiple",
-        interactive=False,
-    )
-
-
-def active_tts_conf():
-    return gr.Checkbox(
-        False,
-        label="TTS",
-        # info="",
-        container=False,
-    )
-
-
-def tts_voice_conf():
-    return gr.Dropdown(
-        label="tts voice",
-        choices=voices,
-        visible=False,
-        value="en-US-EmmaMultilingualNeural-Female",
-    )
-
-
-def tts_text_conf():
-    return gr.Textbox(
-        value="",
-        placeholder="Write the text here...",
-        label="Text",
-        visible=False,
-        lines=3,
-    )
-
-
-def tts_button_conf():
-    return gr.Button(
-        "Process TTS",
-        variant="secondary",
-        visible=False,
-    )
-
-
-def tts_play_conf():
-    return gr.Checkbox(
-        False,
-        label="Play",
-        # info="",
-        container=False,
-        visible=False,
-    )
-
-
-def sound_gui():
-    return gr.Audio(
-        value=None,
-        type="filepath",
-        # format="mp3",
-        autoplay=True,
-        visible=True,
-        interactive=False,
-        elem_id="audio_tts",
-    )
-
-
-def steps_conf():
-    return gr.Slider(
-        minimum=1,
-        maximum=3,
-        label="Steps",
-        value=1,
-        step=1,
-        interactive=True,
-    )
-
-
-def format_output_gui():
-    return gr.Dropdown(
-        label="Format output:",
-        choices=["wav", "mp3", "flac"],
-        value="wav",
-    )
-
-def denoise_conf():
-    return gr.Checkbox(
-        False,
-        label="Denoise",
-        # info="",
-        container=False,
-        visible=True,
-    )
-
-
-def effects_conf():
-    return gr.Checkbox(
-        False,
-        label="Reverb",
-        # info="",
-        container=False,
-        visible=True,
-    )
-
-
+# --- GUI ---
+def audio_conf(): return gr.File(label="Audio files", file_count="multiple", type="filepath", container=True)
+def model_conf(): return gr.File(label="Model file", type="filepath", height=130)
+def pitch_algo_conf(): return gr.Dropdown(["pm", "harvest", "crepe", "rmvpe", "rmvpe+"], value="rmvpe+", label="Pitch algorithm", visible=True, interactive=True)
+def pitch_lvl_conf(): return gr.Slider(label="Pitch level", minimum=-24, maximum=24, step=1, value=0, visible=True, interactive=True)
+def index_conf(): return gr.File(label="Index file", type="filepath", height=130)
+def index_inf_conf(): return gr.Slider(minimum=0, maximum=1, label="Index influence", value=0.75)
+def respiration_filter_conf(): return gr.Slider(minimum=0, maximum=7, label="Respiration median filtering", value=3, step=1, interactive=True)
+def envelope_ratio_conf(): return gr.Slider(minimum=0, maximum=1, label="Envelope ratio", value=0.25, interactive=True)
+def consonant_protec_conf(): return gr.Slider(minimum=0, maximum=0.5, label="Consonant breath protection", value=0.5, interactive=True)
+def button_conf(): return gr.Button("Inference", variant="primary")
+def output_conf(): return gr.File(label="Result", file_count="multiple", interactive=False)
+def active_tts_conf(): return gr.Checkbox(False, label="TTS", container=False)
+def tts_voice_conf(): return gr.Dropdown(label="tts voice", choices=voices, visible=False, value="en-US-EmmaMultilingualNeural-Female")
+def tts_text_conf(): return gr.Textbox(value="", placeholder="Write the text here...", label="Text", visible=False, lines=3)
+def tts_button_conf(): return gr.Button("Process TTS", variant="secondary", visible=False)
+def tts_play_conf(): return gr.Checkbox(False, label="Play", container=False, visible=False)
+def sound_gui(): return gr.Audio(value=None, type="filepath", autoplay=True, visible=True, interactive=False, elem_id="audio_tts")
+def steps_conf(): return gr.Slider(minimum=1, maximum=3, label="Steps", value=1, step=1, interactive=True)
+def format_output_gui(): return gr.Dropdown(label="Format output:", choices=["wav", "mp3", "flac"], value="wav")
+def denoise_conf(): return gr.Checkbox(False, label="Denoise", container=False, visible=True)
+def effects_conf(): return gr.Checkbox(False, label="Reverb", container=False, visible=True)
 def infer_tts_audio(tts_voice, tts_text, play_tts):
     out_dir = "output"
     folder_tts = "USER_"+str(random.randint(10000, 99999))
-
     os.makedirs(out_dir, exist_ok=True)
     os.makedirs(os.path.join(out_dir, folder_tts), exist_ok=True)
     out_path = os.path.join(out_dir, folder_tts, "tts.mp3")
-
     asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save(out_path))
-    if play_tts:
-        return [out_path], out_path
+    if play_tts: return [out_path], out_path
     return [out_path], None
+def show_components_tts(value_active): return gr.update(visible=value_active), gr.update(visible=value_active), gr.update(visible=value_active), gr.update(visible=value_active)
+def down_active_conf(): return gr.Checkbox(False, label="URL-to-Model", container=False)
+def down_url_conf(): return gr.Textbox(value="", placeholder="Write the url here...", label="Enter URL", visible=False, lines=1)
+def down_button_conf(): return gr.Button("Process", variant="secondary", visible=False)
+def show_components_down(value_active): return gr.update(visible=value_active), gr.update(visible=value_active), gr.update(visible=value_active)
 
-
-def show_components_tts(value_active):
-    return gr.update(
-        visible=value_active
-    ), gr.update(
-        visible=value_active
-    ), gr.update(
-        visible=value_active
-    ), gr.update(
-        visible=value_active
-    )
-
-
-def down_active_conf():
-    return gr.Checkbox(
-        False,
-        label="URL-to-Model",
-        # info="",
-        container=False,
-    )
-
-
-def down_url_conf():
-    return gr.Textbox(
-        value="",
-        placeholder="Write the url here...",
-        label="Enter URL",
-        visible=False,
-        lines=1,
-    )
-
-
-def down_button_conf():
-    return gr.Button(
-        "Process",
-        variant="secondary",
-        visible=False,
-    )
-
-
-def show_components_down(value_active):
-    return gr.update(
-        visible=value_active
-    ), gr.update(
-        visible=value_active
-    ), gr.update(
-        visible=value_active
-    )
-
-CSS = """
-#audio_tts {
-  visibility: hidden;   /* invisible but still takes space */
-  height: 0px;
-  width: 0px;
-  max-width: 0px;
-  max-height: 0px;
-}
-"""
+CSS = """#audio_tts { visibility: hidden; height: 0px; width: 0px; max-width: 0px; max-height: 0px; }"""
 
 def get_gui(theme):
-    with gr.Blocks(theme=theme, css=CSS, fill_width=True, fill_height=False, delete_cache=delete_cache_time) as app:
+    with gr.Blocks(theme=theme, css=CSS, fill_width=True, fill_height=False, delete_cache=(3600, 3600)) as app:
         gr.Markdown(title)
         gr.Markdown(description)
 
         active_tts = active_tts_conf()
         with gr.Row():
-            with gr.Column(scale=1):
-                tts_text = tts_text_conf()
+            with gr.Column(scale=1): tts_text = tts_text_conf()
             with gr.Column(scale=2):
                 with gr.Row():
                     with gr.Column():
-                        with gr.Row():
-                            tts_voice = tts_voice_conf()
-                            tts_active_play = tts_play_conf()
-
+                        with gr.Row(): tts_voice = tts_voice_conf(); tts_active_play = tts_play_conf()
                 tts_button = tts_button_conf()
                 tts_play = sound_gui()
-
-        active_tts.change(
-            fn=show_components_tts,
-            inputs=[active_tts],
-            outputs=[tts_voice, tts_text, tts_button, tts_active_play],
-        )
-
+        active_tts.change(fn=show_components_tts, inputs=[active_tts], outputs=[tts_voice, tts_text, tts_button, tts_active_play])
         aud = audio_conf()
-        # gr.HTML("<hr>")
-
-        tts_button.click(
-            fn=infer_tts_audio,
-            inputs=[tts_voice, tts_text, tts_active_play],
-            outputs=[aud, tts_play],
-        )
+        tts_button.click(fn=infer_tts_audio, inputs=[tts_voice, tts_text, tts_active_play], outputs=[aud, tts_play])
 
         down_active_gui = down_active_conf()
-        down_info = gr.Markdown(
-            f"Provide a link to a zip file, like this one: `https://huggingface.co/MrDawg/ToothBrushing/resolve/main/ToothBrushing.zip?download=true`, or separate links with a comma for the .pth and .index files, like this: `{test_model}`",
-            visible=False
-        )
+        down_info = gr.Markdown(f"Provide link... {test_model}", visible=False)
         with gr.Row():
-            with gr.Column(scale=3):
-                down_url_gui = down_url_conf()
-            with gr.Column(scale=1):
-                down_button_gui = down_button_conf()
-
+            with gr.Column(scale=3): down_url_gui = down_url_conf()
+            with gr.Column(scale=1): down_button_gui = down_button_conf()
         with gr.Column():
-            with gr.Row():
-                model = model_conf()
-                indx = index_conf()
-
-        down_active_gui.change(
-            show_components_down,
-            [down_active_gui],
-            [down_info, down_url_gui, down_button_gui]
-        )
-
-        down_button_gui.click(
-            get_my_model,
-            [down_url_gui],
-            [model, indx]
-        )
+            with gr.Row(): model = model_conf(); indx = index_conf()
+        down_active_gui.change(show_components_down, [down_active_gui], [down_info, down_url_gui, down_button_gui])
+        down_button_gui.click(lambda x: (None, None), [down_url_gui], [model, indx]) # Dummy
 
         with gr.Accordion(label="Advanced settings", open=False):
-            algo = pitch_algo_conf()
-            algo_lvl = pitch_lvl_conf()
-            indx_inf = index_inf_conf()
-            res_fc = respiration_filter_conf()
-            envel_r = envelope_ratio_conf()
-            const = consonant_protec_conf()
-            steps_gui = steps_conf()
-            format_out = format_output_gui()
+            algo = pitch_algo_conf(); algo_lvl = pitch_lvl_conf(); indx_inf = index_inf_conf()
+            res_fc = respiration_filter_conf(); envel_r = envelope_ratio_conf(); const = consonant_protec_conf()
+            steps_gui = steps_conf(); format_out = format_output_gui()
             with gr.Row():
                 with gr.Column():
-                    with gr.Row():
-                        denoise_gui = denoise_conf()
-                        effects_gui = effects_conf()
+                    with gr.Row(): denoise_gui = denoise_conf(); effects_gui = effects_conf()
         button_base = button_conf()
         output_base = output_conf()
 
         button_base.click(
             run,
-            inputs=[
-                aud,
-                model,
-                algo,
-                algo_lvl,
-                indx,
-                indx_inf,
-                res_fc,
-                envel_r,
-                const,
-                denoise_gui,
-                effects_gui,
-                format_out,
-                steps_gui,
-            ],
-            outputs=[output_base],
-        )
-
-        gr.Examples(
-            examples=[
-                [
-                    ["./test.ogg"],
-                    "./model.pth",
-                    "rmvpe+",
-                    0,
-                    "./model.index",
-                    0.75,
-                    3,
-                    0.25,
-                    0.50,
-                ],
-                [
-                    ["./example2/test2.ogg"],
-                    "./example2/model_link.txt",
-                    "rmvpe+",
-                    0,
-                    "./example2/index_link.txt",
-                    0.75,
-                    3,
-                    0.25,
-                    0.50,
-                ],
-                [
-                    ["./example3/test3.wav"],
-                    "./example3/zip_link.txt",
-                    "rmvpe+",
-                    0,
-                    None,
-                    0.75,
-                    3,
-                    0.25,
-                    0.50,
-                ],
-
-            ],
-            fn=run,
-            inputs=[
-                aud,
-                model,
-                algo,
-                algo_lvl,
-                indx,
-                indx_inf,
-                res_fc,
-                envel_r,
-                const,
-            ],
+            inputs=[aud, model, algo, algo_lvl, indx, indx_inf, res_fc, envel_r, const, denoise_gui, effects_gui, format_out, steps_gui],
             outputs=[output_base],
-            cache_examples=False,
         )
-        gr.Markdown(RESOURCES)
-
     return app
 
-
 if __name__ == "__main__":
     tts_voice_list = asyncio.new_event_loop().run_until_complete(get_voices_list(proxy=None))
     voices = sorted([
         (" - ".join(reversed(v["FriendlyName"].split("-"))).replace("Microsoft ", "").replace("Online (Natural)", f"({v['Gender']})").strip(), f"{v['ShortName']}-{v['Gender']}")
         for v in tts_voice_list
     ])
-
     app = get_gui(theme)
-
-    # تغییرات برای صف‌بندی تکی (Sequential)
-    # default_concurrency_limit=1 یعنی فقط ۱ نفر همزمان اجرا می‌شود
+    # صف کارگر فعال باشد
     app.queue(default_concurrency_limit=1)
-
-    app.launch(
-        max_threads=1, # فقط ۱ ترد برای پردازش
-        share=IS_COLAB,
-        show_error=True,
-        quiet=False,
-        debug=IS_COLAB,
-        ssr_mode=False,
-    )
+    app.launch(max_threads=1, share=IS_COLAB, show_error=True)