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

# منابع ضروری
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')
        if not os.path.exists(init_path):
            for site_pkg_path in site.getsitepackages():
                potential_path = os.path.join(site_pkg_path, 'LangSegment', '__init__.py')
                if os.path.exists(potential_path):
                    init_path = potential_path
                    break
            else: return

        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")
else:
    if not os.getcwd().endswith("Amphion"):
        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}")
        raise 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")
print(f"Using device: {device}")

inference_pipelines = {}

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

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:
        # --- 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 and content_data.shape[1] > 1:
            content_data = np.mean(content_data, axis=1)
        
        content_tensor = torch.FloatTensor(content_data).unsqueeze(0)
        if content_sr != 24000:
            content_tensor = torchaudio.functional.resample(content_tensor, content_sr, 24000)
            content_sr = 24000
        content_tensor = content_tensor / (torch.max(torch.abs(content_tensor)) + 1e-6) * 0.95

        # --- 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 and ref_data.shape[1] > 1:
            ref_data = np.mean(ref_data, axis=1)

        ref_tensor = torch.FloatTensor(ref_data).unsqueeze(0)
        if ref_sr != 24000:
            ref_tensor = torchaudio.functional.resample(ref_tensor, ref_sr, 24000)
            ref_sr = 24000
        ref_tensor = ref_tensor / (torch.max(torch.abs(ref_tensor)) + 1e-6) * 0.95
        
        if ref_tensor.shape[1] > 24000 * 20:
             ref_tensor = ref_tensor[:, :24000 * 20]

        save_audio_pcm16(ref_tensor, temp_reference_path, ref_sr)
        
        # --- منطق حرفه‌ای Warm-up Context Stitching ---
        pipeline = get_pipeline()
        
        SR = 24000
        STEP_SIZE = 10 * SR        # هر 10 ثانیه جلو می‌رویم
        WARMUP_SIZE = 3 * SR       # 3 ثانیه کانتکست (نگاه به عقب) برای گرم شدن
        CROSSFADE_SIZE = 1 * SR    # 1 ثانیه میکس برای نرم کردن اتصال
        
        total_samples = content_tensor.shape[1]
        print(f"[{session_id}] Duration: {total_samples/SR:.2f}s. Studio Mode (Warm-up + Crossfade)...")
        
        final_audio = []
        previous_tail = None # نگهداری ۱ ثانیه آخر تکه قبلی برای میکس
        
        # حلقه روی تکه‌ها
        current_pos = 0
        
        while current_pos < total_samples:
            # محاسبه دقیق بازه ورودی
            # اگر اولین تکه نیستیم، 3 ثانیه عقب‌تر شروع می‌کنیم (Warm-up)
            if current_pos == 0:
                start_input = 0
                warmup_cut = 0
            else:
                start_input = max(0, current_pos - WARMUP_SIZE)
                warmup_cut = current_pos - start_input # مقداری که باید از اول خروجی دور بریزیم
            
            # پایان این تکه (10 ثانیه جلوتر + 1 ثانیه اضافه برای میکس بعدی)
            end_input = min(current_pos + STEP_SIZE + CROSSFADE_SIZE, total_samples)
            
            # اگر دیتایی نمانده، تمام
            if start_input >= end_input:
                break

            # استخراج تکه ورودی
            chunk_tensor = content_tensor[:, start_input:end_input]
            save_audio_pcm16(chunk_tensor, temp_content_path, SR)
            
            print(f"[{session_id}] Processing chunk starting at {current_pos/SR:.1f}s (with context)")
            
            try:
                gen = pipeline.inference_fm(
                    src_wav_path=temp_content_path,
                    timbre_ref_wav_path=temp_reference_path,
                    flow_matching_steps=64, # کیفیت 64 پله‌ای
                )
                
                if torch.isnan(gen).any(): gen = torch.nan_to_num(gen, nan=0.0)
                if gen.dim() == 1: gen = gen.unsqueeze(0)
                gen = gen.cpu().squeeze(0).numpy()
                
                # 1. حذف قسمت Warm-up (که قبلاً ساخته شده بود)
                if warmup_cut > 0:
                    # اما صبر کن! ما باید CROSSFADE_SIZE تا قبل از نقطه برش را نگه داریم برای میکس
                    # پس برش را کمی عقب‌تر می‌زنیم تا همپوشانی داشته باشیم
                    valid_start = warmup_cut - CROSSFADE_SIZE
                    if valid_start < 0: valid_start = 0 # نباید پیش بیاد
                    gen = gen[valid_start:]
                
                # الان `gen` شامل: [همپوشانی با قبلی] + [تکه جدید] + [همپوشانی با بعدی] است.
                
                # 2. میکس با تکه قبلی (اگر وجود دارد)
                if previous_tail is not None:
                    # جدا کردن قسمت همپوشانی از این تکه
                    overlap_part = gen[:CROSSFADE_SIZE]
                    new_part = gen[CROSSFADE_SIZE:]
                    
                    # اگر سایزها یکی بود میکس کن
                    if len(overlap_part) == len(previous_tail):
                        alpha = np.linspace(0, 1, len(overlap_part))
                        blended = (previous_tail * (1 - alpha)) + (overlap_part * alpha)
                        final_audio.append(blended)
                    else:
                        # فال‌بک (نباید پیش بیاد)
                        final_audio.append(previous_tail)
                    
                    # حالا قسمت جدید را پردازش می‌کنیم
                    # باید قسمت انتهایی را برای دور بعد ذخیره کنیم
                    if len(new_part) > CROSSFADE_SIZE and end_input < total_samples:
                        # ذخیره دم برای دور بعد
                        previous_tail = new_part[-CROSSFADE_SIZE:]
                        # اضافه کردن بدنه اصلی
                        final_audio.append(new_part[:-CROSSFADE_SIZE])
                    else:
                        # تکه آخر است، کلش را اضافه کن
                        final_audio.append(new_part)
                        previous_tail = None
                
                else:
                    # تکه اول است
                    if len(gen) > CROSSFADE_SIZE and end_input < total_samples:
                        previous_tail = gen[-CROSSFADE_SIZE:]
                        final_audio.append(gen[:-CROSSFADE_SIZE])
                    else:
                        final_audio.append(gen)
                        previous_tail = None
                
                current_pos += STEP_SIZE
                
            except Exception as e:
                print(f"Error in chunk: {e}")
                # در صورت خطا، پرش کن (بهتر از قطع شدن است)
                current_pos += STEP_SIZE
                # اضافه کردن سکوت
                final_audio.append(np.zeros(STEP_SIZE))
                previous_tail = None

        # چسباندن نهایی
        if len(final_audio) > 0:
            full_audio = np.concatenate(final_audio)
        else:
            full_audio = np.zeros(24000)

        save_audio_pcm16(full_audio, 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 (Studio)") as demo:
    gr.Markdown("## Vevo-Timbre: Zero-Shot Voice Conversion")
    gr.Markdown("نسخه استودیویی: بدون پرش، بدون تداخل زمانی.")
    
    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()