app.py

import os
import sys
import importlib.util
import site
import json
import torch
import gradio as gr
import torchaudio
import numpy as np
from huggingface_hub import snapshot_download, hf_hub_download
import subprocess
import re
import spaces
import uuid
import soundfile as sf

# --- تنظیمات و نصب ---
downloaded_resources = {
    "configs": False,
    "tokenizer_vq8192": False,
    "fmt_Vq8192ToMels": False,
    "vocoder": False
}

def install_espeak():
    try:
        result = subprocess.run(["which", "espeak-ng"], capture_output=True, text=True)
        if result.returncode != 0:
            print("Installing espeak-ng...")
            subprocess.run(["apt-get", "update"], check=True)
            subprocess.run(["apt-get", "install", "-y", "espeak-ng", "espeak-ng-data"], check=True)
    except Exception as e:
        print(f"Error installing espeak-ng: {e}")

install_espeak()

def patch_langsegment_init():
    try:
        spec = importlib.util.find_spec("LangSegment")
        if spec is None or spec.origin is None: return
        init_path = os.path.join(os.path.dirname(spec.origin), '__init__.py')
        with open(init_path, 'r') as f: lines = f.readlines()
        modified = False
        new_lines = []
        target_line_prefix = "from .LangSegment import"
        for line in lines:
            if line.strip().startswith(target_line_prefix) and ('setLangfilters' in line or 'getLangfilters' in line):
                mod_line = line.replace(',setLangfilters', '').replace(',getLangfilters', '')
                mod_line = mod_line.replace('setLangfilters,', '').replace('getLangfilters,', '').rstrip(',') 
                new_lines.append(mod_line + '\n')
                modified = True
            else:
                new_lines.append(line)
        if modified:
            with open(init_path, 'w') as f: f.writelines(new_lines)
            try:
                import LangSegment
                importlib.reload(LangSegment)
            except: pass
    except: pass

patch_langsegment_init()

if not os.path.exists("Amphion"):
    subprocess.run(["git", "clone", "https://github.com/open-mmlab/Amphion.git"])
    os.chdir("Amphion")
if os.path.dirname(os.path.abspath("Amphion")) not in sys.path:
    sys.path.append(os.path.dirname(os.path.abspath("Amphion")))

os.makedirs("wav", exist_ok=True)
os.makedirs("ckpts/Vevo", exist_ok=True)

from models.vc.vevo.vevo_utils import VevoInferencePipeline

def save_audio_pcm16(waveform, output_path, sample_rate=24000):
    try:
        if isinstance(waveform, torch.Tensor):
            waveform = waveform.detach().cpu()
            if waveform.dim() == 2 and waveform.shape[0] == 1:
                waveform = waveform.squeeze(0)
            waveform = waveform.numpy()
        sf.write(output_path, waveform, sample_rate, subtype='PCM_16')
    except Exception as e:
        print(f"Save error: {e}")

def setup_configs():
    if downloaded_resources["configs"]: return
    config_path = "models/vc/vevo/config"
    os.makedirs(config_path, exist_ok=True)
    config_files = ["Vq8192ToMels.json", "Vocoder.json"]
    for file in config_files:
        file_path = f"{config_path}/{file}"
        if not os.path.exists(file_path):
            try:
                file_data = hf_hub_download(repo_id="amphion/Vevo", filename=f"config/{file}", repo_type="model")
                subprocess.run(["cp", file_data, file_path])
            except Exception as e: print(f"Error downloading config {file}: {e}")
    downloaded_resources["configs"] = True

setup_configs()
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

inference_pipelines = {}

def preload_all_resources():
    setup_configs()
    global downloaded_content_style_tokenizer_path, downloaded_fmt_path, downloaded_vocoder_path
    if not downloaded_resources["tokenizer_vq8192"]:
        downloaded_content_style_tokenizer_path = snapshot_download(repo_id="amphion/Vevo", repo_type="model", cache_dir="./ckpts/Vevo", allow_patterns=["tokenizer/vq8192/*"])
        downloaded_resources["tokenizer_vq8192"] = True
    if not downloaded_resources["fmt_Vq8192ToMels"]:
        downloaded_fmt_path = snapshot_download(repo_id="amphion/Vevo", repo_type="model", cache_dir="./ckpts/Vevo", allow_patterns=["acoustic_modeling/Vq8192ToMels/*"])
        downloaded_resources["fmt_Vq8192ToMels"] = True
    if not downloaded_resources["vocoder"]:
        downloaded_vocoder_path = snapshot_download(repo_id="amphion/Vevo", repo_type="model", cache_dir="./ckpts/Vevo", allow_patterns=["acoustic_modeling/Vocoder/*"])
        downloaded_resources["vocoder"] = True

downloaded_content_style_tokenizer_path = None
downloaded_fmt_path = None
downloaded_vocoder_path = None
preload_all_resources()

def get_pipeline():
    if "timbre" in inference_pipelines: return inference_pipelines["timbre"]
    pipeline = VevoInferencePipeline(
        content_style_tokenizer_ckpt_path=os.path.join(downloaded_content_style_tokenizer_path, "tokenizer/vq8192"),
        fmt_cfg_path="./models/vc/vevo/config/Vq8192ToMels.json",
        fmt_ckpt_path=os.path.join(downloaded_fmt_path, "acoustic_modeling/Vq8192ToMels"),
        vocoder_cfg_path="./models/vc/vevo/config/Vocoder.json",
        vocoder_ckpt_path=os.path.join(downloaded_vocoder_path, "acoustic_modeling/Vocoder"),
        device=device,
    )
    inference_pipelines["timbre"] = pipeline
    return pipeline

@spaces.GPU()
def vevo_timbre(content_wav, reference_wav):
    session_id = str(uuid.uuid4())[:8]
    temp_content_path = f"wav/c_{session_id}.wav"
    temp_reference_path = f"wav/r_{session_id}.wav"
    output_path = f"wav/out_{session_id}.wav"
    
    if content_wav is None or reference_wav is None:
        raise ValueError("Please upload audio files")
    
    try:
        SR = 24000
        
        # --- 1. پردازش ورودی ---
        if isinstance(content_wav, tuple):
            content_sr, content_data = content_wav if isinstance(content_wav[0], int) else (content_wav[1], content_wav[0])
        else:
            content_sr, content_data = content_wav
        if len(content_data.shape) > 1: content_data = np.mean(content_data, axis=1)
        
        content_tensor = torch.FloatTensor(content_data).unsqueeze(0)
        if content_sr != SR:
            content_tensor = torchaudio.functional.resample(content_tensor, content_sr, SR)
        content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95
        content_full_np = content_tensor.squeeze().numpy()

        # --- 2. پردازش رفرنس ---
        if isinstance(reference_wav, tuple):
            ref_sr, ref_data = reference_wav if isinstance(reference_wav[0], int) else (reference_wav[1], reference_wav[0])
        else:
            ref_sr, ref_data = reference_wav
        if len(ref_data.shape) > 1: ref_data = np.mean(ref_data, axis=1)
        
        ref_tensor = torch.FloatTensor(ref_data).unsqueeze(0)
        if ref_sr != SR:
            ref_tensor = torchaudio.functional.resample(ref_tensor, ref_sr, SR)
        ref_tensor = ref_tensor / (torch.max(torch.abs(ref_tensor)) + 1e-6) * 0.95
        if ref_tensor.shape[1] > SR * 20: ref_tensor = ref_tensor[:, :SR * 20]
        save_audio_pcm16(ref_tensor, temp_reference_path, SR)
        
        # --- 3. استراتژی جوش دادن Equal Power (500ms) ---
        pipeline = get_pipeline()
        
        # تنظیمات حیاتی
        CHUNK_DURATION = 10.0   # طول خالص هر تکه
        CROSSFADE_SEC = 0.5     # طول هم‌پوشانی (نیم ثانیه برای حذف لرزش)
        
        chunk_samples = int(CHUNK_DURATION * SR)
        crossfade_samples = int(CROSSFADE_SEC * SR)
        total_samples = len(content_full_np)
        
        final_output = np.array([], dtype=np.float32)
        
        # ایجاد منحنی فید Equal Power (سینوسی)
        # این منحنی باعث می‌شود حجم صدا در محل اتصال ثابت بماند
        fade_out_curve = np.cos(np.linspace(0, np.pi/2, crossfade_samples))
        fade_in_curve = np.sin(np.linspace(0, np.pi/2, crossfade_samples))
        
        # شروع حلقه پردازش
        # ما در هر مرحله به اندازه chunk_samples جلو می‌رویم
        # اما برای ورودی مدل، crossfade_samples را از قبل هم برمی‌داریم
        
        cursor = 0
        print(f"[{session_id}] Processing with 500ms Equal-Power Crossfade...")
        
        while cursor < total_samples:
            # تعیین بازه ورودی برای مدل
            # اگر اولین تکه نیست، باید کمی از عقب‌تر شروع کنیم (برای هم‌پوشانی)
            is_first_chunk = (cursor == 0)
            
            start_idx = cursor
            if not is_first_chunk:
                start_idx -= crossfade_samples  # عقب‌گرد برای هم‌پوشانی
                
            end_idx = min(total_samples, cursor + chunk_samples)
            
            # اگر به انتهای فایل رسیدیم و تکه خیلی کوچک است
            if start_idx >= end_idx:
                break
                
            current_chunk_input = content_full_np[start_idx:end_idx]
            
            # ذخیره و اجرا
            save_audio_pcm16(torch.FloatTensor(current_chunk_input).unsqueeze(0), temp_content_path, SR)
            
            try:
                gen = pipeline.inference_fm(
                    src_wav_path=temp_content_path,
                    timbre_ref_wav_path=temp_reference_path,
                    flow_matching_steps=64,
                )
                if torch.isnan(gen).any(): gen = torch.nan_to_num(gen, nan=0.0)
                gen_np = gen.detach().cpu().squeeze().numpy()
                
                # --- عملیات میکس هوشمند ---
                
                if is_first_chunk:
                    # تکه اول: مستقیماً اضافه کن
                    final_output = np.concatenate([final_output, gen_np])
                else:
                    # تکه‌های بعدی:
                    # 1. بخش هم‌پوشانی (Crossfade Area)
                    # 2. بخش جدید (New Area)
                    
                    if len(gen_np) < crossfade_samples:
                        # اگر خروجی خیلی کوتاه بود (نادر)، فقط بچسبان
                        final_output = np.concatenate([final_output, gen_np])
                    else:
                        # جدا کردن بخش میکس و بخش جدید از خروجی فعلی
                        overlap_part_new = gen_np[:crossfade_samples]
                        rest_part_new = gen_np[crossfade_samples:]
                        
                        # جدا کردن بخش میکس از انتهای خروجی قبلی
                        if len(final_output) >= crossfade_samples:
                            overlap_part_old = final_output[-crossfade_samples:]
                            
                            # فرمول Equal Power Crossfade
                            # Old * Cos + New * Sin
                            blended = (overlap_part_old * fade_out_curve) + (overlap_part_new * fade_in_curve)
                            
                            # جایگزینی انتهای آرایه اصلی با بخش میکس شده
                            final_output[-crossfade_samples:] = blended
                            
                            # اضافه کردن باقی‌مانده
                            final_output = np.concatenate([final_output, rest_part_new])
                        else:
                            # اگر بافر قبلی خیلی کوتاه بود (نباید پیش بیاید)
                            final_output = np.concatenate([final_output, gen_np])

            except Exception as e:
                print(f"Error at {cursor}: {e}")
                # در صورت خطا سکوت اضافه کن
                missing = end_idx - start_idx
                final_output = np.concatenate([final_output, np.zeros(missing)])
            
            # حرکت به جلو
            cursor += chunk_samples

        save_audio_pcm16(final_output, output_path, SR)
        return output_path

    finally:
        if os.path.exists(temp_content_path): os.remove(temp_content_path)
        if os.path.exists(temp_reference_path): os.remove(temp_reference_path)

with gr.Blocks(title="Vevo-Timbre (Pro Stitch)") as demo:
    gr.Markdown("## Vevo-Timbre: Zero-Shot Voice Conversion")
    gr.Markdown("Professional Stitching: 500ms Equal-Power Crossfade (No Jitter, No Ghosting).")
    
    with gr.Row():
        with gr.Column():
            timbre_content = gr.Audio(label="Source Audio", type="numpy")
            timbre_reference = gr.Audio(label="Target Timbre", type="numpy")
            timbre_button = gr.Button("Generate", variant="primary")
        with gr.Column():
            timbre_output = gr.Audio(label="Result")
            
    timbre_button.click(vevo_timbre, inputs=[timbre_content, timbre_reference], outputs=timbre_output)

demo.launch()