Deploy Gradio app with multiple files

app.py

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+import gradio as gr
+import time
+import os
+import shutil
+from typing import Dict, Any, List, Tuple
+from config import LOCAL_STRINGS, TEMP_DIR, DEFAULT_RVC_MODEL_PATH, LANGUAGES
+from utils import get_localized_strings, clean_file_paths
+from models import rvc_training_mock, rvc_conversion_mock, tts_inference
+
+# Global state to store the selected language
+current_lang_state = gr.State("en")
+# Global state to track generated model file path for cleanup
+trained_model_file_path = gr.State(DEFAULT_RVC_MODEL_PATH)
+# State for uploaded RVC model path in conversion tab
+converted_model_path = gr.State(None)
+
+def update_ui_labels(lang: str) -> Dict[str, Any]:
+    """Updates all component labels based on the selected language."""
+    strings = get_localized_strings(lang)
+    
+    updates = {
+        # Tabs
+        "tab_train": gr.Tab(label=strings["tab_train"]),
+        "tab_convert": gr.Tab(label=strings["tab_convert"]),
+        "tab_tts": gr.Tab(label=strings["tab_tts"]),
+
+        # Header/Language Selector
+        "app_title": gr.Markdown(f"# {strings['title']}"),
+        "lang_radio": gr.Radio(label=strings["lang_select"], value=lang),
+        "subtitle": gr.Markdown(
+            f"[{strings['subtitle']}]({strings['subtitle_link']})"
+        ),
+        
+        # Training Tab
+        "train_desc": gr.Markdown(strings["train_desc"]),
+        "train_input_audio": gr.Audio(label=strings["train_input_audio"]),
+        "train_input_name": gr.Textbox(label=strings["train_input_name"]),
+        "train_btn": gr.Button(strings["train_btn"], variant="primary"),
+        "train_output_file": gr.File(label=strings["train_output_file"], visible=False),
+        "train_status": gr.Textbox(label=strings["train_status"], interactive=False),
+        
+        # Conversion Tab
+        "convert_desc": gr.Markdown(strings["convert_desc"]),
+        "convert_input_singer": gr.Audio(label=strings["convert_input_singer"]),
+        "convert_input_model_file": gr.File(label=strings["convert_input_model"], visible=True),
+        "convert_pitch": gr.Slider(label=strings["convert_pitch"]),
+        "convert_index_rate": gr.Slider(label=strings["convert_index_rate"]),
+        "convert_btn": gr.Button(strings["convert_btn"], variant="primary"),
+        "convert_output": gr.Audio(label=strings["convert_output"]),
+
+        # TTS Tab
+        "tts_desc": gr.Markdown(strings["tts_desc"]),
+        "tts_input": gr.Textbox(label=strings["tts_input"], placeholder="Type your text here...", lines=3),
+        "tts_speed": gr.Slider(label=strings["tts_speed"]),
+        "tts_btn": gr.Button(strings["tts_btn"], variant="primary"),
+        "tts_output": gr.Audio(label=strings["tts_output"]),
+    }
+    return updates
+
+# --- Gradio Application ---
+
+with gr.Blocks(theme=gr.themes.Base(), css="""
+    .container {
+        max-width: 1000px;
+        margin: auto;
+    }
+    .rtl {
+        direction: rtl;
+        text-align: right;
+    }
+    .rtl label {
+        float: right !important;
+    }
+    .rtl .markdown {
+        text-align: right;
+    }
+""") as demo:
+    
+    gr.HTML("<div class='container' id='app_title_container'></div>") # Placeholder for title Markdown
+
+    # Define language selector and global state
+    with gr.Row(elem_id="lang_row"):
+        lang_radio = gr.Radio(
+            ["en", "ar"], 
+            value="en", 
+            label=LOCAL_STRINGS["en"]["lang_select"], 
+            elem_id="lang_radio",
+            scale=0
+        )
+        subtitle = gr.Markdown(
+            f"[{LOCAL_STRINGS['en']['subtitle']}]({LOCAL_STRINGS['en']['subtitle_link']})",
+            scale=1, 
+            elem_classes=["subtitle-link"]
+        )
+
+    # Placeholders for dynamic components
+    global_components = {}
+
+    with gr.Blocks(elem_id="main_content") as content_block:
+        
+        # Training Tab
+        with gr.Tab(label=LOCAL_STRINGS["en"]["tab_train"], elem_id="tab_train") as tab_train:
+            global_components["tab_train"] = tab_train
+            
+            train_desc = gr.Markdown(LOCAL_STRINGS["en"]["train_desc"])
+            global_components["train_desc"] = train_desc
+
+            with gr.Row():
+                train_input_audio = gr.Audio(
+                    sources=["upload", "microphone"], 
+                    type="filepath", 
+                    label=LOCAL_STRINGS["en"]["train_input_audio"]
+                )
+                global_components["train_input_audio"] = train_input_audio
+
+                train_input_name = gr.Textbox(
+                    label=LOCAL_STRINGS["en"]["train_input_name"],
+                    value="MyVoiceModel"
+                )
+                global_components["train_input_name"] = train_input_name
+
+            train_btn = gr.Button(LOCAL_STRINGS["en"]["train_btn"], variant="primary")
+            global_components["train_btn"] = train_btn
+
+            train_status = gr.Textbox(
+                label=LOCAL_STRINGS["en"]["train_status"], 
+                interactive=False
+            )
+            global_components["train_status"] = train_status
+
+            train_output_file = gr.File(
+                label=LOCAL_STRINGS["en"]["train_output_file"], 
+                visible=False,
+                type="filepath",
+                file_count="single"
+            )
+            global_components["train_output_file"] = train_output_file
+
+        # Conversion Tab
+        with gr.Tab(label=LOCAL_STRINGS["en"]["tab_convert"], elem_id="tab_convert") as tab_convert:
+            global_components["tab_convert"] = tab_convert
+
+            convert_desc = gr.Markdown(LOCAL_STRINGS["en"]["convert_desc"])
+            global_components["convert_desc"] = convert_desc
+            
+            with gr.Row():
+                convert_input_singer = gr.Audio(
+                    sources=["upload", "microphone"],
+                    type="filepath",
+                    label=LOCAL_STRINGS["en"]["convert_input_singer"]
+                )
+                global_components["convert_input_singer"] = convert_input_singer
+
+                convert_input_model_file = gr.File(
+                    label=LOCAL_STRINGS["en"]["convert_input_model"], 
+                    file_types=[".pth"],
+                    type="file"
+                )
+                global_components["convert_input_model_file"] = convert_input_model_file
+
+            with gr.Row():
+                convert_pitch = gr.Slider(
+                    minimum=-12, 
+                    maximum=12, 
+                    step=1, 
+                    value=0, 
+                    label=LOCAL_STRINGS["en"]["convert_pitch"]
+                )
+                global_components["convert_pitch"] = convert_pitch
+
+                convert_index_rate = gr.Slider(
+                    minimum=0.0, 
+                    maximum=1.0, 
+                    step=0.05, 
+                    value=0.7, 
+                    label=LOCAL_STRINGS["en"]["convert_index_rate"]
+                )
+                global_components["convert_index_rate"] = convert_index_rate
+            
+            convert_btn = gr.Button(LOCAL_STRINGS["en"]["convert_btn"], variant="primary")
+            global_components["convert_btn"] = convert_btn
+
+            convert_output = gr.Audio(
+                label=LOCAL_STRINGS["en"]["convert_output"],
+                interactive=False
+            )
+            global_components["convert_output"] = convert_output
+
+        # TTS Tab
+        with gr.Tab(label=LOCAL_STRINGS["en"]["tab_tts"], elem_id="tab_tts") as tab_tts:
+            global_components["tab_tts"] = tab_tts
+
+            tts_desc = gr.Markdown(LOCAL_STRINGS["en"]["tts_desc"])
+            global_components["tts_desc"] = tts_desc
+
+            tts_input = gr.Textbox(
+                label=LOCAL_STRINGS["en"]["tts_input"], 
+                placeholder="The quick brown fox jumps over the lazy dog.", 
+                lines=3
+            )
+            global_components["tts_input"] = tts_input
+
+            with gr.Row():
+                tts_speed = gr.Slider(
+                    minimum=0.5,
+                    maximum=1.5,
+                    step=0.1,
+                    value=1.0,
+                    label=LOCAL_STRINGS["en"]["tts_speed"]
+                )
+                global_components["tts_speed"] = tts_speed
+            
+            tts_btn = gr.Button(LOCAL_STRINGS["en"]["tts_btn"], variant="primary")
+            global_components["tts_btn"] = tts_btn
+
+            tts_output = gr.Audio(
+                label=LOCAL_STRINGS["en"]["tts_output"], 
+                interactive=False
+            )
+            global_components["tts_output"] = tts_output
+
+    # --- Event Handlers ---
+    
+    # 1. Localization Handler
+    def set_language(lang: str) -> Dict[str, Any]:
+        """Sets the language state and updates UI components."""
+        updates = update_ui_labels(lang)
+        
+        # Apply RTL class if Arabic is selected
+        if lang == "ar":
+            updates["app_title_container"] = gr.HTML(
+                f"<div class='container rtl' id='app_title_container'># {LOCAL_STRINGS['ar']['title']}</div>"
+            )
+            
+            # Apply classes to the main blocks content
+            updates["main_content"] = gr.Blocks(elem_classes=["rtl"])
+            
+            # Need to explicitly set the subtitle markdown value and update its class
+            updates["subtitle"] = gr.Markdown(
+                f"[{LOCAL_STRINGS['ar']['subtitle']}]({LOCAL_STRINGS['ar']['subtitle_link']})",
+                elem_classes=["subtitle-link", "rtl"]
+            )
+        else:
+            updates["app_title_container"] = gr.HTML(
+                f"<div class='container' id='app_title_container'># {LOCAL_STRINGS['en']['title']}</div>"
+            )
+            updates["main_content"] = gr.Blocks(elem_classes=[])
+            updates["subtitle"] = gr.Markdown(
+                f"[{LOCAL_STRINGS['en']['subtitle']}]({LOCAL_STRINGS['en']['subtitle_link']})",
+                elem_classes=["subtitle-link"]
+            )
+
+        # Update the state last
+        updates["current_lang_state"] = lang
+        return updates
+
+    # Define the outputs for the language change event
+    localization_outputs = [
+        lang_radio, subtitle, content_block,
+        gr.Update(value=lang_radio.value, **global_components["train_input_audio"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["train_input_name"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["train_btn"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["train_status"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["train_output_file"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["convert_input_singer"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["convert_input_model_file"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["convert_pitch"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["convert_index_rate"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["convert_btn"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["convert_output"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["tts_input"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["tts_speed"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["tts_btn"].__dict__),
+        gr.Update(value=lang_radio.value, **global_components["tts_output"].__dict__),
+        current_lang_state,
+        
+        # Markdown updates
+        train_desc, convert_desc, tts_desc,
+        tab_train, tab_convert, tab_tts,
+        
+        # Invisible HTML component to handle the title update outside blocks
+        gr.HTML(visible=False, elem_id="app_title_container"),
+    ]
+
+    lang_radio.change(
+        set_language, 
+        inputs=[lang_radio], 
+        outputs=localization_outputs,
+        queue=False,
+        show_progress="hidden"
+    )
+
+    # 2. Training Handler
+    def handle_training_output(model_path: str, log_message: str, current_model_state: str) -> Tuple[str, str, gr.Update, str]:
+        """Handles the complex output of the training mock, saving the path and providing the download file."""
+        if not model_path:
+            # Training failed or conditions not met
+            return log_message, None, gr.File(visible=False), current_model_state
+
+        return log_message, model_path, gr.File(value=model_path, visible=True, label=get_localized_strings(current_lang_state.value)['train_output_file']), model_path
+
+    train_btn.click(
+        fn=rvc_training_mock,
+        inputs=[train_input_audio, train_input_name, current_lang_state],
+        outputs=[train_status, train_output_file, train_output_file, trained_model_file_path] # The last output updates the global state
+    )
+
+    # 3. Conversion Handler
+    convert_btn.click(
+        fn=rvc_conversion_mock,
+        inputs=[
+            convert_input_singer,
+            convert_input_model_file, # Gradio passes FileData object/dict
+            convert_pitch,
+            convert_index_rate,
+            current_lang_state
+        ],
+        outputs=convert_output
+    )
+
+    # 4. TTS Handler
+    tts_btn.click(
+        fn=tts_inference,
+        inputs=[tts_input, current_lang_state, tts_speed],
+        outputs=tts_output
+    )
+
+    # Initial UI setup (must happen after components are defined)
+    # We run the language setter once to initialize the title and main content structure
+    initial_updates = set_language(lang_radio.value)
+    demo.load(
+        lambda: tuple(initial_updates.values()), 
+        outputs=list(initial_updates.keys()), 
+        queue=False,
+        show_progress="hidden"
+    )
+
+    # Cleanup handler when the session closes
+    demo.unload(
+        lambda path_list: clean_file_paths(path_list),
+        inputs=[
+            gr.List([train_input_audio, train_input_name, train_output_file, convert_input_singer, trained_model_file_path])
+        ],
+        queue=False
+    )
+
+if __name__ == "__main__":
+    demo.launch()

config.py

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+import os
+from typing import Dict, Any, List
+
+# --- Paths and Constants ---
+TEMP_DIR = "temp_models"
+os.makedirs(TEMP_DIR, exist_ok=True)
+
+DEFAULT_RVC_MODEL_PATH = os.path.join(TEMP_DIR, "placeholder_rvc_model.pth")
+DEFAULT_SR = 48000
+
+# --- Localization Data (English and Arabic) ---
+
+LOCAL_STRINGS: Dict[str, Dict[str, Any]] = {
+    "en": {
+        "title": "RVC Voice Cloning and Conversion Suite",
+        "subtitle": "Built with anycoder",
+        "subtitle_link": "https://huggingface.co/spaces/akhaliq/anycoder",
+        "lang_select": "Select Language",
+        "tab_train": "1. Voice Cloning (Training)",
+        "tab_convert": "2. Voice Conversion (Singing)",
+        "tab_tts": "3. Text-to-Speech",
+        "tts_desc": "Generate speech using a general AI voice model.",
+        "tts_input": "Text Input",
+        "tts_output": "Generated Speech",
+        "tts_btn": "Generate Speech",
+        "train_desc": "Upload 1-5 minutes of clear voice audio to create your clone. Output file is downloadable.",
+        "train_input_audio": "Upload Voice Sample Audio (WAV/MP3)",
+        "train_input_name": "Model Name (e.g., my_voice)",
+        "train_btn": "Start Voice Training (Mock)",
+        "train_output_file": "Download Trained Model (.pth)",
+        "train_status": "Training Status/Log",
+        "convert_desc": "Convert a singer's voice in an audio file to your cloned voice.",
+        "convert_input_singer": "Upload Singer Audio (WAV/MP3) to Convert",
+        "convert_input_model": "Load Trained Voice Model (.pth file)",
+        "convert_pitch": "Pitch Change (Semitones)",
+        "convert_index_rate": "Index Rate (Higher = More Fidelity to Target Voice)",
+        "convert_btn": "Perform Voice Conversion",
+        "convert_output": "Converted Audio Output",
+        "voice_select": "Select Target Voice Model",
+        "tts_speed": "Speech Speed (1.0 = Normal)",
+        "tts_voice": "TTS Voice Speaker (Default)",
+    },
+    "ar": {
+        "title": "حزمة تحويل واستنساخ الصوت RVC",
+        "subtitle": "مبني بواسطة anycoder",
+        "subtitle_link": "https://huggingface.co/spaces/akhaliq/anycoder",
+        "lang_select": "اختر اللغة",
+        "tab_train": "1. استنساخ الصوت (التدريب)",
+        "tab_convert": "2. تحويل الصوت (الغناء)",
+        "tab_tts": "3. تحويل النص إلى كلام",
+        "tts_desc": "إنشاء كلام باستخدام نموذج صوتي عام للذكاء الاصطناعي.",
+        "tts_input": "إدخال النص",
+        "tts_output": "الكلام الناتج",
+        "tts_btn": "توليد الكلام",
+        "train_desc": "قم بتحميل 1-5 دقائق من الصوت الواضح لإنشاء نسختك. يمكن تحميل الملف الناتج مباشرة.",
+        "train_input_audio": "تحميل عينة صوتية للتدريب (WAV/MP3)",
+        "train_input_name": "اسم النموذج (مثال: صوتي)",
+        "train_btn": "بدء تدريب الصوت (محاكاة)",
+        "train_output_file": "تحميل النموذج المدرب (.pth)",
+        "train_status": "حالة / سجل التدريب",
+        "convert_desc": "تحويل صوت المغني في ملف صوتي إلى صوتك المستنسخ.",
+        "convert_input_singer": "تحميل صوت المغني المراد تحويله (WAV/MP3)",
+        "convert_input_model": "تحميل نموذج الصوت المدرب (ملف .pth)",
+        "convert_pitch": "تغيير حدة الصوت (نغمات نصفية)",
+        "convert_index_rate": "معدل الفهرس (أعلى = ولاء أكبر للصوت الهدف)",
+        "convert_btn": "تنفيذ تحويل الصوت",
+        "convert_output": "إخراج الصوت المحول",
+        "voice_select": "اختيار نموذج الصوت الهدف",
+        "tts_speed": "سرعة الكلام (1.0 = عادي)",
+        "tts_voice": "المتحدث (افتراضي)",
+    },
+}
+
+# Supported languages
+LANGUAGES = ["en", "ar"]
+
+# TTS configuration (using a small, general-purpose TTS model)
+TTS_MODEL_ID = "facebook/fastspeech2-en-ljspeech"
+TTS_VOCODER_ID = "facebook/hifigan-en-ljspeech"
+
+# Audio normalization factor for simulation (16-bit PCM max)
+MAX_WAV_VALUE = 32767

models.py

@@ -0,0 +1,188 @@
+import torch
+import numpy as np
+import time
+import os
+import gradio as gr
+import spaces
+import torchaudio
+from transformers import AutoProcessor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from utils import get_localized_strings, generate_mock_pth, log_status, load_audio_from_path
+from config import (
+    TTS_MODEL_ID, TTS_VOCODER_ID, DEFAULT_SR, 
+    MAX_WAV_VALUE, TEMP_DIR, DEFAULT_RVC_MODEL_PATH
+)
+
+# --- TTS Setup ---
+try:
+    tts_processor = AutoProcessor.from_pretrained("microsoft/speecht5_tts")
+    tts_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+    tts_vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+    
+    # Load a speaker embedding (e.g., female speaker 7)
+    embeddings_dataset = torchaudio.datasets.LJSPEECH(root="./")
+    speaker_embeddings = torch.tensor(embeddings_dataset[7][1]).unsqueeze(0)
+
+except Exception as e:
+    print(f"Warning: Could not load full TTS models. Falling back to dummy functions. Error: {e}")
+    # Define placeholder variables if models fail to load
+    tts_model = None
+    tts_processor = None
+    tts_vocoder = None
+    speaker_embeddings = None
+
+if torch.cuda.is_available():
+    device = "cuda"
+    if tts_model:
+        tts_model.to(device)
+        tts_vocoder.to(device)
+else:
+    device = "cpu"
+
+# --- Core Functions ---
+
+@spaces.GPU(duration=120)
+def tts_inference(text: str, lang: str, speed: float) -> Tuple[int, np.ndarray]:
+    """
+    Performs Text-to-Speech using the loaded model.
+    """
+    if not tts_model:
+        # Dummy output if models are not available
+        strings = get_localized_strings(lang)
+        text = strings["tts_output"]
+        dummy_audio = np.random.randint(-1000, 1000, size=int(DEFAULT_SR * 2), dtype=np.int16)
+        return DEFAULT_SR, dummy_audio
+        
+    try:
+        inputs = tts_processor(text=text, return_tensors="pt")
+        
+        # Adjust speed (simple approach by modifying duration factors in inputs)
+        # Note: True speed control requires modification of the underlying speech generation
+        # model or using a different library. We rely on the model's default behavior here.
+        
+        inputs = inputs.to(device)
+
+        with torch.no_grad():
+            speech = tts_model.generate_speech(
+                inputs["input_ids"],
+                speaker_embeddings if speaker_embeddings is not None else None,
+                vocoder=tts_vocoder
+            )
+
+        # Convert tensor to numpy array and scale to 16-bit PCM
+        audio_data = speech.cpu().numpy()
+        
+        # Rescale float audio (-1.0 to 1.0) to int16 format
+        audio_int16 = (audio_data * MAX_WAV_VALUE).astype(np.int16)
+        
+        return TTS_VOCODER_ID, audio_int16 # Output sample rate of hifigan is 24000
+    
+    except Exception as e:
+        print(f"TTS Inference Error: {e}")
+        raise gr.Error(f"TTS failed: {str(e)}")
+
+
+@spaces.GPU(duration=180) # RVC training can be very long, simulating speedup
+def rvc_training_mock(audio_file_path: str, model_name: str, lang: str, progress=gr.Progress()) -> Tuple[str, str]:
+    """
+    Simulates RVC model training. Creates a downloadable file.
+    """
+    strings = get_localized_strings(lang)
+
+    if not audio_file_path:
+        raise gr.Error(strings["train_input_audio"] + " " + strings["tts_input"] + " " + strings["tts_output"])
+
+    progress(0, desc=log_status(lang, "Starting audio analysis..."))
+    
+    # 1. Simulate data preparation and feature extraction
+    try:
+        sr, audio_data = load_audio_from_path(audio_file_path)
+        total_duration = len(audio_data) / sr
+    except Exception as e:
+        raise gr.Error(f"Audio file error: {e}")
+
+    if total_duration < 30:
+        return None, log_status(lang, "Audio duration too short for training (Min 30s recommended)")
+
+    # 2. Simulate training steps (e.g., 5 steps)
+    progress(0.1, desc=log_status(lang, "Analyzing input features..."))
+    time.sleep(2)
+    
+    for i in range(1, 6):
+        progress(0.1 + i * 0.15, desc=log_status(lang, "Simulating training epoch {i}/5...", i=i))
+        time.sleep(3)
+
+    # 3. Generate mock .pth file
+    progress(0.9, desc=log_status(lang, "Finalizing model and generating file..."))
+    model_path = generate_mock_pth(model_name, TEMP_DIR)
+    
+    if not model_path:
+        raise gr.Error(log_status(lang, "Error creating model file."))
+
+    final_log = log_status(lang, "Training complete. Model saved to: {path}", path=model_path)
+    progress(1.0, desc=final_log)
+    
+    # Return the path for the gr.File component to handle the download link
+    return model_path, final_log
+
+
+@spaces.GPU(duration=60)
+def rvc_conversion_mock(
+    singer_audio_file: str, 
+    model_file: Dict[str, Any], 
+    pitch_change: int, 
+    index_rate: float,
+    lang: str,
+    progress=gr.Progress()
+) -> Tuple[int, np.ndarray]:
+    """
+    Simulates RVC voice conversion from a singer track using the cloned model.
+    """
+    strings = get_localized_strings(lang)
+
+    if not singer_audio_file:
+        raise gr.Error(strings["convert_input_singer"] + " " + strings["tts_input"])
+    
+    model_path = get_rvc_model_path(model_file, "Simulated Model")
+
+    progress(0, desc=log_status(lang, "Starting conversion process..."))
+
+    # 1. Load input audio
+    try:
+        sr, input_audio = load_audio_from_path(singer_audio_file)
+    except Exception as e:
+        raise gr.Error(f"Audio file error: {e}")
+
+    # 2. Simulate conversion steps
+    progress(0.2, desc=log_status(lang, "Extracting source features and pitch ({pitch} ST)", pitch=pitch_change))
+    time.sleep(3)
+    
+    progress(0.5, desc=log_status(lang, "Applying RVC Index (Rate: {rate})", rate=index_rate))
+    time.sleep(4)
+    
+    # 3. Generate simulated converted audio: 
+    # For a real RVC, this uses the model features. 
+    # Here, we generate random noise, mix it with the original audio, 
+    # and adjust volume/pitch slightly based on parameters.
+    
+    input_audio_float = input_audio.astype(np.float32) / MAX_WAV_VALUE
+    
+    # Simple simulation: Apply pitch shift (FFT-based manipulation is complex in numpy)
+    # Instead, we apply a small delay/reverb and modulate the volume
+    noise = np.random.normal(0, 0.1, len(input_audio_float))
+    converted_audio_float = input_audio_float * (1 + 0.1 * index_rate) + noise
+
+    # Simple pitch simulation: increase amplitude slightly if pitch is high
+    if pitch_change > 0:
+        converted_audio_float *= (1 + pitch_change / 30.0)
+
+    # Normalize output
+    max_val = np.max(np.abs(converted_audio_float))
+    if max_val > 1.0:
+        converted_audio_float /= max_val
+    
+    converted_audio_int16 = (converted_audio_float * MAX_WAV_VALUE).astype(np.int16)
+    
+    progress(1.0, desc=log_status(lang, "Conversion complete."))
+    
+    # Return the converted audio
+    return sr, converted_audio_int16

requirements.txt

@@ -0,0 +1,7 @@
+gradio
+torch
+torchaudio
+numpy
+librosa
+transformers
+accelerate

utils.py

@@ -0,0 +1,66 @@
+import os
+import shutil
+import time
+import numpy as np
+from typing import Literal, Dict, Any, Tuple
+from config import LOCAL_STRINGS, DEFAULT_SR
+
+def get_localized_strings(lang: Literal["en", "ar"]) -> Dict[str, Any]:
+    """Retrieves the localized strings dictionary."""
+    return LOCAL_STRINGS.get(lang, LOCAL_STRINGS["en"])
+
+def log_status(lang: Literal["en", "ar"], message_key: str, **kwargs) -> str:
+    """Logs a localized status message."""
+    strings = get_localized_strings(lang)
+    message = strings.get(message_key, message_key).format(**kwargs)
+    return f"[{time.strftime('%H:%M:%S')}] {message}"
+
+def generate_mock_pth(model_name: str, temp_dir: str) -> str:
+    """Simulates RVC model creation and returns the path to the dummy .pth file."""
+    # Ensure temporary directory exists
+    os.makedirs(temp_dir, exist_ok=True)
+    
+    # Create a unique, descriptive path for the model file
+    filename = f"{model_name}_{int(time.time())}.pth"
+    model_path = os.path.join(temp_dir, filename)
+
+    # Simulate writing a small placeholder model file (real models are MBs/GBs)
+    try:
+        with open(model_path, 'w') as f:
+            f.write(f"RVC Model Data: {model_name}, Training Simulated at {time.ctime()}")
+        return model_path
+    except IOError:
+        # Handle potential permissions issues during file writing
+        return None
+
+def clean_file_paths(paths: List[str]):
+    """Cleans up the temporary files created during the session."""
+    for path in paths:
+        if path and os.path.exists(path):
+            try:
+                os.remove(path)
+            except Exception as e:
+                print(f"Error cleaning up file {path}: {e}")
+
+def get_rvc_model_path(model_file_data: dict, model_name: str) -> str:
+    """
+    Retrieves the actual file path from the Gradio FileData object or uses a default.
+    Gradio components return paths or FileData dicts upon upload.
+    """
+    if model_file_data and isinstance(model_file_data, dict) and 'path' in model_file_data:
+        return model_file_data['path']
+    
+    # Fallback to a placeholder if no file is explicitly uploaded (for demo purposes)
+    return DEFAULT_RVC_MODEL_PATH
+
+def load_audio_from_path(file_path: str) -> Tuple[int, np.ndarray]:
+    """Loads audio file using librosa, resampling to DEFAULT_SR."""
+    import librosa
+    try:
+        audio, sr = librosa.load(file_path, sr=DEFAULT_SR, mono=True)
+        # Convert to 16-bit PCM integer format for standard Gradio audio tuple
+        audio_int16 = (audio * MAX_WAV_VALUE).astype(np.int16)
+        return DEFAULT_SR, audio_int16
+    except Exception as e:
+        print(f"Error loading audio file {file_path}: {e}")
+        raise gr.Error(f"Failed to load audio: {str(e)}")