app.py

import gradio as gr
import subprocess
import os 
import shutil
import tempfile
import torch
import logging
import numpy as np
import re
from concurrent.futures import ThreadPoolExecutor
from functools import lru_cache

# 로깅 설정
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('yue_generation.log'),
        logging.StreamHandler()
    ]
)

# 가사 분석 함수
def analyze_lyrics(lyrics):
    # 줄 단위로 분리
    lines = [line.strip() for line in lyrics.split('\n') if line.strip()]
    
    # 섹션 카운트
    sections = {
        'verse': 0,
        'chorus': 0,
        'bridge': 0,
        'total_lines': len(lines)
    }
    
    current_section = None
    section_lines = {
        'verse': 0,
        'chorus': 0,
        'bridge': 0
    }
    
    for line in lines:
        lower_line = line.lower()
        if '[verse]' in lower_line:
            current_section = 'verse'
            sections['verse'] += 1
        elif '[chorus]' in lower_line:
            current_section = 'chorus'
            sections['chorus'] += 1
        elif '[bridge]' in lower_line:
            current_section = 'bridge'
            sections['bridge'] += 1
        elif current_section and line.strip():
            section_lines[current_section] += 1
            
    # 총 섹션 수 계산
    total_sections = sections['verse'] + sections['chorus'] + sections['bridge']
    
    return sections, total_sections, len(lines), section_lines

def calculate_generation_params(lyrics):
    sections, total_sections, total_lines, section_lines = analyze_lyrics(lyrics)
    
    # 기본 토큰 수 계산
    base_tokens_per_line = 200
    verse_tokens = section_lines['verse'] * base_tokens_per_line
    chorus_tokens = section_lines['chorus'] * (base_tokens_per_line * 1.5)  # 코러스는 50% 더 많은 토큰
    bridge_tokens = section_lines['bridge'] * base_tokens_per_line
    
    # 총 토큰 수 계산
    total_tokens = int(verse_tokens + chorus_tokens + bridge_tokens)
    
    # 섹션 기반 세그먼트 수 계산
    num_segments = max(2, min(4, total_sections))
    
    # 토큰 수 제한
    max_tokens = min(32000, max(3000, total_tokens))
    
    return {
        'max_tokens': max_tokens,
        'num_segments': num_segments,
        'sections': sections,
        'section_lines': section_lines
    }

# 언어 감지 및 모델 선택 함수
def detect_and_select_model(text):
    if re.search(r'[\u3131-\u318E\uAC00-\uD7A3]', text):  # 한글
        return "m-a-p/YuE-s1-7B-anneal-jp-kr-cot"
    elif re.search(r'[\u4e00-\u9fff]', text):  # 중국어
        return "m-a-p/YuE-s1-7B-anneal-zh-cot"
    elif re.search(r'[\u3040-\u309F\u30A0-\u30FF]', text):  # 일본어
        return "m-a-p/YuE-s1-7B-anneal-jp-kr-cot"
    else:  # 영어/기타
        return "m-a-p/YuE-s1-7B-anneal-en-cot"

def optimize_model_selection(lyrics, genre):
    model_path = detect_and_select_model(lyrics)
    params = calculate_generation_params(lyrics)
    
    model_config = {
        "m-a-p/YuE-s1-7B-anneal-en-cot": {
            "max_tokens": params['max_tokens'],
            "temperature": 0.8,
            "batch_size": 8,
            "num_segments": params['num_segments'],
            "chorus_strength": 1.2 if params['sections']['chorus'] > 0 else 1.0
        },
        "m-a-p/YuE-s1-7B-anneal-jp-kr-cot": {
            "max_tokens": params['max_tokens'],
            "temperature": 0.7,
            "batch_size": 8,
            "num_segments": params['num_segments'],
            "chorus_strength": 1.2 if params['sections']['chorus'] > 0 else 1.0
        },
        "m-a-p/YuE-s1-7B-anneal-zh-cot": {
            "max_tokens": params['max_tokens'],
            "temperature": 0.7,
            "batch_size": 8,
            "num_segments": params['num_segments'],
            "chorus_strength": 1.2 if params['sections']['chorus'] > 0 else 1.0
        }
    }
    
    return model_path, model_config[model_path], params

# GPU 설정 최적화
def optimize_gpu_settings():
    if torch.cuda.is_available():
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.deterministic = False
        torch.backends.cudnn.enabled = True
        
        torch.cuda.empty_cache()
        torch.cuda.set_device(0)
        
        logging.info(f"Using GPU: {torch.cuda.get_device_name(0)}")
        logging.info(f"Available GPU memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.2f} GB")
    else:
        logging.warning("GPU not available!")

def install_flash_attn():
    try:
        if not torch.cuda.is_available():
            logging.warning("GPU not available, skipping flash-attn installation")
            return False
            
        cuda_version = torch.version.cuda
        if cuda_version is None:
            logging.warning("CUDA not available, skipping flash-attn installation")
            return False
            
        logging.info(f"Detected CUDA version: {cuda_version}")
        
        try:
            import flash_attn
            logging.info("flash-attn already installed")
            return True
        except ImportError:
            logging.info("Installing flash-attn...")
            
        try:
            subprocess.run(
                ["pip", "install", "flash-attn", "--no-build-isolation"],
                check=True,
                capture_output=True
            )
            logging.info("flash-attn installed successfully!")
            return True
        except subprocess.CalledProcessError:
            logging.warning("Failed to install flash-attn via pip, skipping...")
            return False
            
    except Exception as e:
        logging.warning(f"Failed to install flash-attn: {e}")
        return False

def initialize_system():
    optimize_gpu_settings()
    has_flash_attn = install_flash_attn()
    
    from huggingface_hub import snapshot_download
    
    folder_path = './inference/xcodec_mini_infer'
    os.makedirs(folder_path, exist_ok=True)
    logging.info(f"Created folder at: {folder_path}")

    snapshot_download(
        repo_id="m-a-p/xcodec_mini_infer",
        local_dir="./inference/xcodec_mini_infer",
        resume_download=True
    )

    try:
        os.chdir("./inference")
        logging.info(f"Working directory changed to: {os.getcwd()}")
    except FileNotFoundError as e:
        logging.error(f"Directory error: {e}")
        raise

@lru_cache(maxsize=50)
def get_cached_file_path(content_hash, prefix):
    return create_temp_file(content_hash, prefix)

def empty_output_folder(output_dir):
    try:
        shutil.rmtree(output_dir)
        os.makedirs(output_dir)
        logging.info(f"Output folder cleaned: {output_dir}")
    except Exception as e:
        logging.error(f"Error cleaning output folder: {e}")
        raise

def create_temp_file(content, prefix, suffix=".txt"):
    temp_file = tempfile.NamedTemporaryFile(delete=False, mode="w", prefix=prefix, suffix=suffix)
    content = content.strip() + "\n\n"
    content = content.replace("\r\n", "\n").replace("\r", "\n")
    temp_file.write(content)
    temp_file.close()
    logging.debug(f"Temporary file created: {temp_file.name}")
    return temp_file.name

def get_last_mp3_file(output_dir):
    mp3_files = [f for f in os.listdir(output_dir) if f.endswith('.mp3')]
    if not mp3_files:
        logging.warning("No MP3 files found")
        return None
    
    mp3_files_with_path = [os.path.join(output_dir, f) for f in mp3_files]
    mp3_files_with_path.sort(key=os.path.getmtime, reverse=True)
    return mp3_files_with_path[0]

def infer(genre_txt_content, lyrics_txt_content, num_segments, max_new_tokens):
    try:
        # 모델 선택 및 설정
        model_path, config, params = optimize_model_selection(lyrics_txt_content, genre_txt_content)
        logging.info(f"Selected model: {model_path}")
        logging.info(f"Lyrics analysis: {params}")
        
        # 실제 사용할 파라미터
        actual_num_segments = config['num_segments']
        actual_max_tokens = config['max_tokens']
        
        logging.info(f"Using segments: {actual_num_segments}, tokens: {actual_max_tokens}")
        
        # 임시 파일 생성
        genre_txt_path = create_temp_file(genre_txt_content, prefix="genre_")
        lyrics_txt_path = create_temp_file(lyrics_txt_content, prefix="lyrics_")
        
        output_dir = "./output"
        os.makedirs(output_dir, exist_ok=True)
        empty_output_folder(output_dir)

        # 기본 명령어 구성 (불필요한 매개변수 제거)
        command = [
            "python", "infer.py",
            "--stage1_model", model_path,
            "--stage2_model", "m-a-p/YuE-s2-1B-general",
            "--genre_txt", genre_txt_path,
            "--lyrics_txt", lyrics_txt_path,
            "--run_n_segments", str(actual_num_segments),
            "--stage2_batch_size", str(config['batch_size']),
            "--output_dir", output_dir,
            "--cuda_idx", "0",
            "--max_new_tokens", str(actual_max_tokens)
        ]

        # GPU가 있는 경우에만 추가 옵션 적용
        if torch.cuda.is_available():
            command.append("--disable_offload_model")

        # CUDA 환경 변수 설정
        env = os.environ.copy()
        if torch.cuda.is_available():
            env.update({
                "CUDA_VISIBLE_DEVICES": "0",
                "CUDA_HOME": "/usr/local/cuda",
                "PATH": f"/usr/local/cuda/bin:{env.get('PATH', '')}",
                "LD_LIBRARY_PATH": f"/usr/local/cuda/lib64:{env.get('LD_LIBRARY_PATH', '')}",
                "PYTORCH_CUDA_ALLOC_CONF": "max_split_size_mb:512"
            })

        # transformers 캐시 마이그레이션 처리
        try:
            from transformers.utils import move_cache
            move_cache()
        except Exception as e:
            logging.warning(f"Cache migration warning (non-critical): {e}")

        # 명령 실행
        process = subprocess.run(
            command,
            env=env,
            check=False,
            capture_output=True,
            text=True
        )

        # 실행 결과 로깅
        logging.info(f"Command output: {process.stdout}")
        if process.stderr:
            logging.error(f"Command error: {process.stderr}")

        if process.returncode != 0:
            logging.error(f"Command failed with return code: {process.returncode}")
            logging.error(f"Command: {' '.join(command)}")
            raise RuntimeError(f"Inference failed: {process.stderr}")

        # 결과 처리
        last_mp3 = get_last_mp3_file(output_dir)
        if last_mp3:
            logging.info(f"Generated audio file: {last_mp3}")
            return last_mp3
        else:
            logging.warning("No output audio file generated")
            return None

    except Exception as e:
        logging.error(f"Inference error: {e}")
        raise
    finally:
        # 임시 파일 정리
        for file in [genre_txt_path, lyrics_txt_path]:
            try:
                os.remove(file)
                logging.debug(f"Removed temporary file: {file}")
            except Exception as e:
                logging.warning(f"Failed to remove temporary file {file}: {e}")

def main():
    # Gradio 인터페이스
    with gr.Blocks() as demo:
        with gr.Column():
            gr.Markdown("# YuE: Open Music Foundation Models for Full-Song Generation (Multi-Language Support)")

            
            with gr.Row():
                with gr.Column():
                    genre_txt = gr.Textbox(
                        label="Genre",
                        placeholder="Enter music genre and style descriptions..."
                    )
                    lyrics_txt = gr.Textbox(
                        label="Lyrics (Supports English, Korean, Japanese, Chinese)",
                        placeholder="Enter song lyrics with [verse], [chorus], [bridge] tags...",
                        lines=10
                    )
                    
                with gr.Column():
                    num_segments = gr.Number(
                        label="Number of Song Segments (Auto-adjusted based on lyrics)",
                        value=2,
                        minimum=1,
                        maximum=4,
                        step=1,
                        interactive=False
                    )
                    max_new_tokens = gr.Slider(
                        label="Max New Tokens (Auto-adjusted based on lyrics)",
                        minimum=500,
                        maximum=32000,
                        step=500,
                        value=4000,
                        interactive=False
                    )
                    submit_btn = gr.Button("Generate Music", variant="primary")
                    music_out = gr.Audio(label="Generated Audio")

            # 다국어 예제
            gr.Examples(
                examples=[
                    # 영어 예제
                    [
                        "female blues airy vocal bright vocal piano sad romantic guitar jazz",
                        """[verse]
In the quiet of the evening, shadows start to fall
Whispers of the night wind echo through the hall
Lost within the silence, I hear your gentle voice
Guiding me back homeward, making my heart rejoice

[chorus]
Don't let this moment fade, hold me close tonight
With you here beside me, everything's alright
Can't imagine life alone, don't want to let you go
Stay with me forever, let our love just flow

                        """
                    ],
                    # 한국어 예제
                    [
                        "K-pop bright energetic synth dance electronic",
                        """[verse]
빛나는 별들처럼 우리의 꿈이
저 하늘을 수놓아 반짝이네
함께라면 어디든 갈 수 있어
우리의 이야기가 시작되네

[chorus]
달려가자 더 높이 더 멀리
두려움은 없어 너와 함께라면
영원히 계속될 우리의 노래
이 순간을 기억해 forever

                        """
                    ]
                ],
                inputs=[genre_txt, lyrics_txt]
            )

        # 시스템 초기화
        initialize_system()
        
        # 이벤트 핸들러
        submit_btn.click(
            fn=infer,
            inputs=[genre_txt, lyrics_txt, num_segments, max_new_tokens],
            outputs=[music_out]
        )

        return demo

if __name__ == "__main__":
    demo = main()
    demo.queue(max_size=20).launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True,
        show_api=True,
        show_error=True,
        max_threads=2
    )