app-backup.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()
    ]
)

def analyze_lyrics(lyrics, repeat_chorus=2):
    lines = [line.strip() for line in lyrics.split('\n') if line.strip()]
    
    sections = {
        'verse': 0,
        'chorus': 0,
        'bridge': 0,
        'total_lines': len(lines)
    }
    
    current_section = None
    section_lines = {
        'verse': [],
        'chorus': [],
        'bridge': []
    }

    for line in lines:
        lower_line = line.lower()
        if '[verse]' in lower_line:
            current_section = 'verse'
            sections['verse'] += 1
            continue
        elif '[chorus]' in lower_line:
            current_section = 'chorus'
            sections['chorus'] += 1
            continue
        elif '[bridge]' in lower_line:
            current_section = 'bridge'
            sections['bridge'] += 1
            continue

        # 현재 섹션에 라인 추가
        if current_section:
            section_lines[current_section].append(line)

    # 만약 코러스가 1회만 있고, repeat_chorus > 1이면 반복해서 붙이기
    # chorus 섹션 전체 블록을 복제
    if sections['chorus'] == 1 and repeat_chorus > 1:
        chorus_block = section_lines['chorus'][:]
        for _ in range(repeat_chorus - 1):
            section_lines['chorus'].extend(chorus_block)
    
    # 라인 수 재계산
    new_total_lines = sum(len(section_lines[sec]) for sec in section_lines)
    
    return sections, (sections['verse'] + sections['chorus'] + sections['bridge']), new_total_lines, {
        'verse': len(section_lines['verse']),
        'chorus': len(section_lines['chorus']),
        'bridge': len(section_lines['bridge'])
    }


def calculate_generation_params(lyrics):
    sections, total_sections, total_lines, section_lines = analyze_lyrics(lyrics)
    
    # 기본 시간 계산 (초 단위)
    time_per_line = {
        'verse': 4,    # verse는 한 줄당 4초
        'chorus': 6,   # chorus는 한 줄당 6초
        'bridge': 5    # bridge는 한 줄당 5초
    }
    
    # 각 섹션별 예상 시간 계산
    section_durations = {
        'verse': section_lines['verse'] * time_per_line['verse'],
        'chorus': section_lines['chorus'] * time_per_line['chorus'],
        'bridge': section_lines['bridge'] * time_per_line['bridge']
    }
    
    total_duration = sum(section_durations.values())
    total_duration = max(60, total_duration)  # 최소 60초
    
    # 토큰 계산 (더 보수적인 값 사용)
    base_tokens = 3000  # 기본 토큰 수
    tokens_per_line = 200  # 줄당 토큰 수
    
    total_tokens = base_tokens + (total_lines * tokens_per_line)
    
    # 섹션 기반 세그먼트 수 계산
    if sections['chorus'] > 0:
        num_segments = 3  # 코러스가 있는 경우 3개 세그먼트
    else:
        num_segments = 2  # 코러스가 없는 경우 2개 세그먼트
    
    # 토큰 수 제한
    max_tokens = min(8000, total_tokens)  # 최대 8000 토큰으로 제한
    
    return {
        'max_tokens': max_tokens,
        'num_segments': num_segments,
        'sections': sections,
        'section_lines': section_lines,
        'estimated_duration': total_duration,
        'section_durations': section_durations,
        'has_chorus': sections['chorus'] > 0
    }

def get_audio_duration(file_path):
    try:
        import librosa
        duration = librosa.get_duration(path=file_path)
        return duration
    except Exception as e:
        logging.error(f"Failed to get audio duration: {e}")
        return None

# 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"



# 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 optimize_model_selection(lyrics, genre):
    model_path = detect_and_select_model(lyrics)
    params = calculate_generation_params(lyrics)
    
    # 코러스 존재 여부에 따른 설정 조정
    has_chorus = params['sections']['chorus'] > 0
    
    # 토큰 수 계산
    tokens_per_segment = params['max_tokens'] // params['num_segments']
    
    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'],
            "estimated_duration": params['estimated_duration']
        },
        "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'],
            "estimated_duration": params['estimated_duration']
        },
        "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'],
            "estimated_duration": params['estimated_duration']
        }
    }
    
    # 코러스가 있는 경우 토큰 수 증가
    if has_chorus:
        for config in model_config.values():
            config['max_tokens'] = int(config['max_tokens'] * 1.5)  # 50% 더 많은 토큰 할당
    
    return model_path, model_config[model_path], params

def infer(genre_txt_content, lyrics_txt_content, num_segments, max_new_tokens):
    genre_txt_path = None
    lyrics_txt_path = None
    
    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}")
        
        # 코러스 섹션 확인 및 로깅
        has_chorus = params['sections']['chorus'] > 0
        estimated_duration = params.get('estimated_duration', 90)


        # 토큰 수와 세그먼트 수 조정
        if has_chorus:
            actual_max_tokens = min(8000, int(config['max_tokens'] * 1.2))  # 20% 증가, 최대 8000
            actual_num_segments = 3
        else:
            actual_max_tokens = config['max_tokens']
            actual_num_segments = 2
        

        
        logging.info(f"Estimated duration: {estimated_duration} seconds")
        logging.info(f"Has chorus sections: {has_chorus}")
        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
        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", "4",  # 배치 사이즈 감소
            "--output_dir", output_dir,
            "--cuda_idx", "0",
            "--max_new_tokens", str(actual_max_tokens)
        ]

        # GPU 설정
        if torch.cuda.is_available():
            command.append("--disable_offload_model")
        # GPU 설정


        # 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": f"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:
            try:
                duration = get_audio_duration(last_mp3)
                logging.info(f"Generated audio file: {last_mp3}")
                if duration:
                    logging.info(f"Audio duration: {duration:.2f} seconds")
                    logging.info(f"Expected duration: {estimated_duration} seconds")
                    
                    # 생성된 음악이 너무 짧은 경우 경고
                    if duration < estimated_duration * 0.8:
                        logging.warning(f"Generated audio is shorter than expected: {duration:.2f}s < {estimated_duration:.2f}s")
            except Exception as e:
                logging.warning(f"Failed to get audio duration: {e}")
            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
        if genre_txt_path and os.path.exists(genre_txt_path):
            try:
                os.remove(genre_txt_path)
                logging.debug(f"Removed temporary file: {genre_txt_path}")
            except Exception as e:
                logging.warning(f"Failed to remove temporary file {genre_txt_path}: {e}")
        
        if lyrics_txt_path and os.path.exists(lyrics_txt_path):
            try:
                os.remove(lyrics_txt_path)
                logging.debug(f"Removed temporary file: {lyrics_txt_path}")
            except Exception as e:
                logging.warning(f"Failed to remove temporary file {lyrics_txt_path}: {e}")

def main():
    # Gradio 인터페이스
    with gr.Blocks() as demo:
        with gr.Column():
            gr.Markdown("# Open SUNO: 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
                    )
                    with gr.Row():
                        duration_info = gr.Label(label="Estimated Duration")
                        sections_info = gr.Label(label="Section Information")
                    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

[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]
언젠가 마주한 눈빛 속에서
우린 서로를 알아보았지

[chorus]
다시 한 번 내게 말해줘
너의 진심을 숨기지 말아 줘

[verse]
어두운 밤을 지날 때마다
너의 목소리를 떠올려

[chorus]
다시 한 번 내게 말해줘
너의 진심을 숨기지 말아 줘


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

        # 시스템 초기화
        initialize_system()

        def update_info(lyrics):
            if not lyrics:
                return "No lyrics entered", "No sections detected"
            params = calculate_generation_params(lyrics)
            duration = params['estimated_duration']
            sections = params['sections']
            return (
                f"Estimated duration: {duration:.1f} seconds",
                f"Verses: {sections['verse']}, Chorus: {sections['chorus']} (Expected full length including chorus)"
            )

        

        # 이벤트 핸들러
        lyrics_txt.change(
            fn=update_info,
            inputs=[lyrics_txt],
            outputs=[duration_info, sections_info]
        )
        
        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
    )