Update app.py
Browse files
app.py
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import os
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import
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import inspect
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import gradio as gr
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from
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import static_ffmpeg
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#
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if not audio_path or not model_file:
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return None, "
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try:
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print(f"Processing Model: {model_path}")
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rvc = RVCInference(device="cpu")
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rvc.load_model(
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out_path = "/tmp/output_persian_fix.wav"
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if os.path.exists(out_path): os.remove(out_path)
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# کشف پارامترهای تابع (برای سازگاری با نسخههای مختلف)
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sig = inspect.signature(rvc.infer_file)
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params = sig.parameters
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kwargs = {
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"input_path": audio_path,
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"output_path": out_path,
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}
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# 1. پیچ (Pitch)
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if "pitch" in params: kwargs["pitch"] = int(pitch_change)
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elif "f0_up_key" in params: kwargs["f0_up_key"] = int(pitch_change)
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# 2. متد (Method) - حتما RMVPE
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if "method" in params: kwargs["method"] = f0_method
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elif "f0_method" in params: kwargs["f0_method"] = f0_method
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# 3. ایندکس (Index)
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if "index_path" in params: kwargs["index_path"] = index_path
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if "index_rate" in params: kwargs["index_rate"] = float(index_rate)
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# 4. محافظت (Protect) - حیاتی برای فارسی
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if "protect" in params: kwargs["protect"] = float(protect_val)
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# 5. فیلتر (Filter Radius) - برای رفع لرزش صدا
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if "filter_radius" in params: kwargs["filter_radius"] = int(filter_radius)
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# 6. دقت زمانی (Hop Length) - برای کیفیت بهتر
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if "hop_length" in params: kwargs["hop_length"] = 64 # دقت بالاتر (پیشفرض 128 است)
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print(f"Running with: {kwargs}")
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rvc.infer_file(**kwargs)
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return
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except Exception as e:
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return None, f"❌ خطا: {
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#
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with gr.Row():
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with gr.Column():
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gr.
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with gr.Column():
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gr.
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btn.click(
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[audio_in, model_in, index_in, pitch, method, index_rate, protect, filter_radius],
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[audio_out, status]
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)
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import os
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import sys
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import time
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import inspect
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import numpy as np
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import soundfile as sf
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import librosa
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import gradio as gr
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from scipy.signal import butter, lfilter
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# ==========================================
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# 1. تنظیمات اولیه
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# ==========================================
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try:
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import imageio_ffmpeg
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import static_ffmpeg
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from rvc_python.infer import RVCInference
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static_ffmpeg.add_paths()
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ffmpeg_path = imageio_ffmpeg.get_ffmpeg_exe()
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os.environ["PATH"] += os.pathsep + os.path.dirname(ffmpeg_path)
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print("✅ سیستم آماده است.")
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except ImportError as e:
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print(f"❌ خطای ایمپورت: {e}")
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sys.exit(1)
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TEMP_DIR = "/tmp/rvc_studio"
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os.makedirs(TEMP_DIR, exist_ok=True)
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# ==========================================
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# 2. توابع پردازش فرکانس (برای رفع گنگی صدا)
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# ==========================================
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def anti_muffle_filter(y, sr):
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"""فیلتر برای کاهش فرکانسهای گنگ و تو دماغی (400-800Hz)"""
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try:
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from scipy.signal import iirpeak
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# کاهش 3 دسیبل در فرکانس 600Hz
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b, a = iirpeak(600, Q=1.5, fs=sr, ftype='notch')
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return lfilter(b, a, y)
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except:
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return y
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def clarity_boost(y, sr):
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"""تقویت فرکانسهای بالا برای شفافیت بیشتر"""
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try:
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# افزایش 2 دسیبل در فرکانس 8000Hz
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from scipy.signal import iirpeak
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b, a = iirpeak(8000, Q=1.0, fs=sr, ftype='peak')
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return lfilter(b, a, y)
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except:
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return y
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def preprocess_audio(input_path):
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"""پیشپردازش صدا: نرمالسازی و فیلتر ضد گنگ"""
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y, sr = librosa.load(input_path, sr=None, mono=True)
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y = librosa.util.normalize(y) * 0.95
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y = anti_muffle_filter(y, sr)
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processed_path = os.path.join(TEMP_DIR, "preprocessed.wav")
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sf.write(processed_path, y, sr)
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return processed_path, f"✅ پیشپردازش انجام شد (SR: {sr}Hz, Anti-Muffle)"
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def postprocess_audio(input_path):
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"""پسپردازش: تقویت شفافیت"""
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y, sr = librosa.load(input_path, sr=None, mono=True)
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y = clarity_boost(y, sr)
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post_path = os.path.join(TEMP_DIR, "postprocessed.wav")
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sf.write(post_path, y, sr)
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return post_path, "✅ پسپردازش انجام شد (Clarity Boost)"
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# ==========================================
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# 3. موتور اصلی تبدیل
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# ==========================================
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def rvc_process_pipeline(
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audio_path, model_file, index_file, pitch_change, f0_method,
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index_rate, protect_val, filter_radius, resample_sr, envelope_mix, hop_length
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):
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if not audio_path or not model_file:
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return None, "❌ ورودیها کامل نیست."
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try:
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# پیشپردازش
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clean_audio, log1 = preprocess_audio(audio_path)
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# تبدیل RVC
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rvc = RVCInference(device="cpu")
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rvc.load_model(model_file.name)
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rvc_out_path = os.path.join(TEMP_DIR, "rvc_output.wav")
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sig = inspect.signature(rvc.infer_file)
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params = sig.parameters
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kwargs = {"input_path": clean_audio, "output_path": rvc_out_path}
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if "pitch" in params: kwargs["pitch"] = int(pitch_change)
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elif "f0_up_key" in params: kwargs["f0_up_key"] = int(pitch_change)
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if "method" in params: kwargs["method"] = f0_method
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elif "f0_method" in params: kwargs["f0_method"] = f0_method
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if "index_path" in params and index_file: kwargs["index_path"] = index_file.name
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if "index_rate" in params: kwargs["index_rate"] = float(index_rate)
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if "protect" in params: kwargs["protect"] = float(protect_val)
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if "filter_radius" in params: kwargs["filter_radius"] = int(filter_radius)
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if "resample_sr" in params: kwargs["resample_sr"] = int(resample_sr)
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if "rms_mix_rate" in params: kwargs["rms_mix_rate"] = float(envelope_mix)
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if "hop_length" in params: kwargs["hop_length"] = int(hop_length)
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rvc.infer_file(**kwargs)
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log2 = "✅ تبدیل RVC انجام شد."
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# پسپردازش
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final_output, log3 = postprocess_audio(rvc_out_path)
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return final_output, f"{log1}
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{log2}
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{log3}"
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except Exception as e:
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return None, f"❌ خطا: {traceback.format_exc()}"
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# ==========================================
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# 4. رابط کاربری (UI)
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# ==========================================
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with gr.Blocks(title="RVC Studio Pro", theme=gr.themes.Monochrome()) as demo:
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gr.Markdown("# 🎙️ RVC Studio Pro - Edition")
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with gr.Row():
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# ستون ورودی
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with gr.Column():
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audio_in = gr.Audio(label="صدای ورودی", type="filepath")
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with gr.Row():
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model_in = gr.File(label="فایل مدل (.pth)", file_types=[".pth"])
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index_in = gr.File(label="فایل ایندکس (.index)", file_types=[".index"])
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btn = gr.Button("✨ شروع پردازش", variant="primary")
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# ستون خروجی
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with gr.Column():
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audio_out = gr.Audio(label="صدای نهایی")
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status = gr.Textbox(label="گزارش عملیات", lines=4)
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# تبهای تنظیمات
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with gr.Tabs():
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with gr.TabItem("تنظیمات اصلی"):
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pitch = gr.Slider(-24, 24, value=0, step=1, label="Pitch (تغییر گام)")
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method = gr.Dropdown(
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["rmvpe", "harvest", "crepe", "pm"],
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value="harvest",
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label="الگوریتم",
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info="برای فارسی Harvest یا RMVPE بهترین هستند"
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)
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with gr.TabItem("تنظیمات کیفیت (رفع خش و تداخل)"):
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index_rate = gr.Slider(0, 1, value=0.3, label="Index Rate (شدت لهجه مدل)")
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protect = gr.Slider(0, 0.5, value=0.4, label="Protect (محافظت از حروف بیصدا)")
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filter_radius = gr.Slider(3, 7, value=3, label="Filter Radius (نرمکننده لرزش)")
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with gr.TabItem("تنظیمات فرکانس (رفع گنگی صدا)"):
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hop_length = gr.Slider(32, 256, value=64, step=32, label="Hop Length (دقت زمانی)")
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envelope_mix = gr.Slider(0, 1, value=0.2, label="Volume Envelope Mix (میکس حجم صدا)")
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resample_sr = gr.Slider(0, 48000, value=40000, step=8000, label="Resample SR (قفل فرکانس)")
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btn.click(
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rvc_process_pipeline,
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[audio_in, model_in, index_in, pitch, method, index_rate, protect, filter_radius, resample_sr, envelope_mix, hop_length],
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[audio_out, status]
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)
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