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
# اضافه شدن کتابخانه PyDub
from pydub import AudioSegment
import io

# --- نصب و پچ کردن ---
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

# --- توابع کمکی جدید برای PyDub ---
def numpy_to_audiosegment(audio_arr, sample_rate=24000):
    """تبدیل آرایه نامپای (Float32) به آبجکت AudioSegment"""
    # تبدیل به PCM 16-bit
    audio_int16 = (audio_arr * 32767).astype(np.int16)
    # ایجاد فایل در حافظه
    byte_io = io.BytesIO()
    sf.write(byte_io, audio_int16, sample_rate, format='WAV', subtype='PCM_16')
    byte_io.seek(0)
    return AudioSegment.from_wav(byte_io)

def save_audio_pcm16(waveform, output_path, sample_rate=24000):
    # این تابع فقط برای ذخیره فایل‌های موقت ورودی مدل است
    try:
        if isinstance(waveform, torch.Tensor):
            waveform = waveform.detach().cpu().squeeze().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():
    print("Preloading 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
    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:
        # --- 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 != 24000:
            content_tensor = torchaudio.functional.resample(content_tensor, content_sr, 24000)
        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 != 24000:
            ref_tensor = torchaudio.functional.resample(ref_tensor, 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, 24000)
        
        # --- 3. منطق پردازش با استفاده از PyDub ---
        pipeline = get_pipeline()
        SR = 24000
        
        NEW_CHUNK_SEC = 10.0
        CONTEXT_SEC = 3.0
        
        new_chunk_samples = int(NEW_CHUNK_SEC * SR)
        context_samples = int(CONTEXT_SEC * SR)
        total_samples = len(content_full_np)
        
        # ایجاد یک AudioSegment خالی برای جمع‌آوری خروجی نهایی
        final_audio_segment = AudioSegment.empty()
        
        current_cursor = 0
        print(f"[{session_id}] Processing with PyDub stitching...")
        
        while current_cursor < total_samples:
            start_slice = max(0, current_cursor - context_samples)
            end_slice = min(total_samples, current_cursor + new_chunk_samples)
            
            if start_slice >= end_slice: break
                
            chunk_np = content_full_np[start_slice:end_slice]
            save_audio_pcm16(torch.FloatTensor(chunk_np).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()
                
                # محاسبه مقدار برشی (حذف کانتکست تکراری)
                trim_samples = current_cursor - start_slice
                
                if len(gen_np) > trim_samples:
                    valid_part_np = gen_np[trim_samples:]
                    
                    # تبدیل به فرمت PyDub
                    new_segment = numpy_to_audiosegment(valid_part_np, SR)
                    
                    # اتصال:
                    # اگر اولین تکه نیست، یک فید (Crossfade) بسیار کوتاه (5 میلی ثانیه) 
                    # اعمال می‌کنیم تا صدای "تیک" حذف شود.
                    if len(final_audio_segment) > 0:
                        # تکنیک: یک فید بسیار ریز (Crossfade 5ms)
                        # نکته: PyDub برای کراس‌فید نیاز به همپوشانی دارد، اما چون ما کانتکست را دقیق بریدیم،
                        # اینجا از append ساده استفاده می‌کنیم و فقط لبه‌ها را نرم می‌کنیم.
                        
                        # نرم کردن ابتدای تکه جدید (Fade In 5ms)
                        new_segment = new_segment.fade_in(5)
                        # نرم کردن انتهای تکه قبلی (Fade Out 5ms) - (قبلاً انجام شده یا الان انجام میدیم)
                        # در اینجا فقط چسباندن (Append) با فید این کافیست.
                        
                        final_audio_segment += new_segment
                    else:
                        final_audio_segment += new_segment
                
                current_cursor = end_slice
                
            except Exception as e:
                print(f"Error: {e}")
                current_cursor = end_slice # Skip on error

        # ذخیره خروجی نهایی با PyDub
        final_audio_segment.export(output_path, format="wav")
        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 (PyDub)") as demo:
    gr.Markdown("## Vevo-Timbre: Voice Conversion")
    gr.Markdown("Seamless stitching powered by PyDub library.")
    
    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()