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()
    ]
)

################################
# 기존에 정의된 함수 및 로직들  #
################################

def optimize_gpu_settings():
    if torch.cuda.is_available():
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.enabled = True
        torch.backends.cudnn.deterministic = False
        torch.cuda.empty_cache()
        torch.cuda.set_device(0)
        torch.cuda.Stream(0)
        os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:512'
        
        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")
        
        if 'L40S' in torch.cuda.get_device_name(0):
            torch.cuda.set_per_process_memory_fraction(0.95)

import logging

def analyze_lyrics(lyrics, repeat_chorus=2):
    # 먼저 라인별로 분리하고, 공백 줄 제거
    lines = [line.strip() for line in lyrics.split('\n')]
    lines = [line for line in lines if line]
    
    # 만약 전체가 비어있다면 강제로 '.' 한 줄 추가
    if not lines:
        lines = ['.']
    else:
        # 마지막 줄이 [verse], [chorus], [bridge] 태그로만 끝나면
        # 임의로 '.' 한 줄을 추가하여 실제 가사 라인이 되도록 처리
        last_line_lower = lines[-1].lower()
        if last_line_lower in ['[verse]', '[chorus]', '[bridge]']:
            lines.append('.')

    # 기본 섹션 정보
    sections = {
        'verse': 0,
        'chorus': 0,
        'bridge': 0,
        'total_lines': len(lines)
    }

    # 섹션 라인들을 담을 딕셔너리
    section_lines = {
        'verse': [],
        'chorus': [],
        'bridge': []
    }

    current_section = None
    last_section_start = 0

    # [verse], [chorus], [bridge] 태그가 나오면 섹션을 구분하여 라인을 저장
    for i, line in enumerate(lines):
        lower_line = line.lower()

        if '[verse]' in lower_line:
            if current_section is not None:
                section_lines[current_section].extend(lines[last_section_start:i])
            current_section = 'verse'
            sections['verse'] += 1
            last_section_start = i + 1

        elif '[chorus]' in lower_line:
            if current_section is not None:
                section_lines[current_section].extend(lines[last_section_start:i])
            current_section = 'chorus'
            sections['chorus'] += 1
            last_section_start = i + 1

        elif '[bridge]' in lower_line:
            if current_section is not None:
                section_lines[current_section].extend(lines[last_section_start:i])
            current_section = 'bridge'
            sections['bridge'] += 1
            last_section_start = i + 1

    # 마지막 섹션에 남아 있는 라인들을 추가
    if current_section is not None and last_section_start < len(lines):
        section_lines[current_section].extend(lines[last_section_start:])

    # 코러스 반복 처리
    if sections['chorus'] > 0 and repeat_chorus > 1:
        original_chorus = list(section_lines['chorus'])
        for _ in range(repeat_chorus - 1):
            section_lines['chorus'].extend(original_chorus)

    # 섹션별 라인수 로깅
    logging.info(
        f"Section line counts - Verse: {len(section_lines['verse'])}, "
        f"Chorus: {len(section_lines['chorus'])}, "
        f"Bridge: {len(section_lines['bridge'])}"
    )

    # 반환: 섹션 정보, 전체 섹션 수, 전체 라인 수, 각 섹션별 라인 딕셔너리
    return sections, (sections['verse'] + sections['chorus'] + sections['bridge']), len(lines), section_lines



def calculate_generation_params(lyrics):
    sections, total_sections, total_lines, section_lines = analyze_lyrics(lyrics)
    
    time_per_line = {
        'verse': 4,
        'chorus': 6,
        'bridge': 5
    }
    
    section_durations = {}
    for section_type in ['verse', 'chorus', 'bridge']:
        lines_count = len(section_lines[section_type])
        section_durations[section_type] = lines_count * time_per_line[section_type]
    
    total_duration = sum(duration for duration in section_durations.values())
    total_duration = max(60, int(total_duration * 1.2))
    
    base_tokens = 3000
    tokens_per_line = 200
    extra_tokens = 1000
    total_tokens = base_tokens + (total_lines * tokens_per_line) + extra_tokens
    
    if sections['chorus'] > 0:
        num_segments = 4
    else:
        num_segments = 3

    max_tokens = min(12000, total_tokens)
    
    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 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 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 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 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


def detect_and_select_model(text):
    if re.search(r'[\u3131-\u318E\uAC00-\uD7A3]', text):
        return "m-a-p/YuE-s1-7B-anneal-jp-kr-cot"
    elif re.search(r'[\u4e00-\u9fff]', text):
        return "m-a-p/YuE-s1-7B-anneal-zh-cot"
    elif re.search(r'[\u3040-\u309F\u30A0-\u30FF]', text):
        return "m-a-p/YuE-s1-7B-anneal-jp-kr-cot"
    else:
        return "m-a-p/YuE-s1-7B-anneal-en-cot"

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...")
            
        subprocess.run(
            ["pip", "install", "flash-attn", "--no-build-isolation"],
            check=True,
            capture_output=True
        )
        logging.info("flash-attn installed successfully!")
        return True
            
    except Exception as e:
        logging.warning(f"Failed to install flash-attn: {e}")
        return False

def initialize_system():
    optimize_gpu_settings()
    
    with ThreadPoolExecutor(max_workers=4) as executor:
        futures = []
        futures.append(executor.submit(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}")

        futures.append(executor.submit(
            snapshot_download,
            repo_id="m-a-p/xcodec_mini_infer",
            local_dir="./inference/xcodec_mini_infer",
            resume_download=True
        ))
        
        for future in futures:
            future.result()

    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=100)
def get_cached_file_path(content_hash, prefix):
    return create_temp_file(content_hash, prefix)



def optimize_model_selection(lyrics, genre):
    model_path = detect_and_select_model(lyrics)
    params = calculate_generation_params(lyrics)
    
    has_chorus = params['sections']['chorus'] > 0
    
    model_config = {
        "m-a-p/YuE-s1-7B-anneal-en-cot": {
            "max_tokens": params['max_tokens'],
            "temperature": 0.8,
            "batch_size": 16,
            "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": 16,
            "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": 16,
            "num_segments": params['num_segments'],
            "estimated_duration": params['estimated_duration']
        }
    }
    
    if has_chorus:
        for config in model_config.values():
            config['max_tokens'] = int(config['max_tokens'] * 1.5)
    
    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:
        # ---- (1) 화면에는 보이지 않지만, 마지막에 [chorus] bye 삽입 ----
        forced_line = "[chorus] bye"
        tmp_lyrics = lyrics_txt_content.strip()
        # 이미 'bye'가 들어있는지 확인 (원한다면 조건 추가/삭제 가능)
        if forced_line.lower() not in tmp_lyrics.lower():
            tmp_lyrics += "\n" + forced_line
        
        # ---- (2) 강제 삽입된 tmp_lyrics를 통해 모델 최적화/설정 ----
        model_path, config, params = optimize_model_selection(tmp_lyrics, genre_txt_content)
        logging.info(f"Selected model: {model_path}")
        logging.info(f"Lyrics analysis: {params}")
        
        has_chorus = params['sections']['chorus'] > 0
        estimated_duration = params.get('estimated_duration', 90)

        # 세그먼트 및 토큰 수 설정
        if has_chorus:
            actual_max_tokens = min(12000, int(config['max_tokens'] * 1.3))  # 30% 더 많은 토큰
            actual_num_segments = min(5, params['num_segments'] + 2)        # 추가 세그먼트
        else:
            actual_max_tokens = min(10000, int(config['max_tokens'] * 1.2))
            actual_num_segments = min(4, params['num_segments'] + 1)
        
        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}")
        
        genre_txt_path = create_temp_file(genre_txt_content, prefix="genre_")
        # tmp_lyrics(강제 추가된 문자열)을 임시 파일로 저장
        lyrics_txt_path = create_temp_file(tmp_lyrics, prefix="lyrics_")
        
        output_dir = "./output"
        os.makedirs(output_dir, exist_ok=True)
        empty_output_folder(output_dir)

        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", "16",
            "--output_dir", output_dir,
            "--cuda_idx", "0",
            "--max_new_tokens", str(actual_max_tokens),
            "--disable_offload_model"
        ]

        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",
                "CUDA_LAUNCH_BLOCKING": "0"
            })

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

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

        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}")

        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")
                    
                    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:
        for path in [genre_txt_path, lyrics_txt_path]:
            if path and os.path.exists(path):
                try:
                    os.remove(path)
                    logging.debug(f"Removed temporary file: {path}")
                except Exception as e:
                    logging.warning(f"Failed to remove temporary file {path}: {e}")

#####################################
# 아래부터 Gradio UI 및 main() 부분 #
#####################################

def update_info(lyrics):
    """가사 변경 시 추정 정보를 업데이트하는 함수."""
    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)"
    )

def main():
    # 시스템 초기화 (필요한 모델 다운로드/설치 등)
    initialize_system()

    with gr.Blocks(css="""
        /* 전체 배경 및 컨테이너 스타일 */
        body {
            background-color: #f5f5f5;
        }
        .gradio-container {
            max-width: 1000px;
            margin: auto !important;
            background-color: #ffffff;
            border-radius: 8px;
            padding: 20px;
            box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);
        }
        h1, h2, h3 {
            margin: 0;
            padding: 0;
        }
        p {
            margin: 5px 0;
        }
        /* 예제 블록 스타일 */
        .gr-examples {
            background-color: #fafafa;
            border-radius: 8px;
            padding: 10px;
        }
    """) as demo:

        # 상단 헤더
        gr.HTML("""
        <div style="text-align: center; margin-bottom: 1.5rem;">
            <h1>Open SUNO: Full-Song Generation (Multi-Language Support)</h1>
            <p style="font-size: 1.1rem; color: #555;">
                Enter your song details below and let the AI handle the music production!
            </p>
        </div>
        """)

        # 예제 음악 플레이어 추가
        gr.HTML("""
        <div style="padding: 1rem; margin-bottom: 1.5rem; background-color: #f8f9fa; border-radius: 8px; text-align: center;">
            <h3 style="margin: 0;">Sample Generated Music</h3>
            <p style="color: #666; margin: 5px 0;">Listen to this example</p>
        </div>
        """)
        gr.Audio("metal.mp3", label="Sample Music")

        with gr.Row():
            # 왼쪽 입력 컬럼
            with gr.Column():
                genre_txt = gr.Textbox(
                    label="Genre",
                    placeholder="Enter music genre and style descriptions...",
                    lines=2
                )
                lyrics_txt = gr.Textbox(
                    label="Lyrics (Supports English, Korean, Japanese, Chinese)",
                    placeholder="Enter song lyrics with [verse], [chorus], [bridge] tags...",
                    lines=10
                )

            # 오른쪽 설정/정보 컬럼
            with gr.Column():
                # 여기서 gr.Box -> gr.Group로 변경
                with gr.Group():
                    gr.Markdown("### Generation Settings")
                    num_segments = gr.Number(
                        label="Number of Song Segments (Auto-adjusted)",
                        value=2,
                        minimum=1,
                        maximum=4,
                        step=1,
                        interactive=False
                    )
                    max_new_tokens = gr.Slider(
                        label="Max New Tokens (Auto-adjusted)",
                        minimum=500,
                        maximum=32000,
                        step=500,
                        value=4000,
                        interactive=False
                    )
                
                # 여기서도 gr.Box -> gr.Group로 변경
                with gr.Group():
                    gr.Markdown("### Song Info")
                    duration_info = gr.Label(label="Estimated Duration")
                    sections_info = gr.Label(label="Section Information")
                
                submit_btn = gr.Button("Generate Music", variant="primary")

        # 아래도 gr.Box -> gr.Group로 변경
        with gr.Group():
            music_out = gr.Audio(label="Generated Audio")
        
        # 예시
        gr.Examples(
            examples=[
                [
        "Pop catchy uplifting romantic love song",
        """
[verse]
Under the city lights, your hand in mine
Every step we take, feels like a sign

[chorus]
Baby, you're my everything, my heart is yours
"""
                ],
    
                [
        "K-pop upbeat youthful synth electronic",
        """
[verse] 
노을 속에 너의 기억이 떠올라
[chorus] 
어디든 네 곁에 내가 있을게
[bridge] 
멀리라도 널 위해 달려갈게
"""
                ],
        
                [
        "J-pop energetic emotional dance synth",
        """
[verse]
夜の街に光る君の笑顔
どんな時もそばにいるよ

[chorus]
この気持ち止められない
"""
                ],
    
                [
        "Mandopop sentimental ballad love song piano",
        """
[verse]
夜色温柔像你的拥抱
心跳随着你慢慢变高

[chorus]
永远不要放开我的手
"""
                ]
            ],
            inputs=[genre_txt, lyrics_txt],
            outputs=[]
        )

        # 가사 변경 시 추정 정보 업데이트
        lyrics_txt.change(
            fn=update_info,
            inputs=[lyrics_txt],
            outputs=[duration_info, sections_info]
        )

        # 버튼 클릭 시 infer 실행
        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=8
    )