Delete app-backup.py

app-backup.py

@@ -1,548 +0,0 @@
-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 optimize_gpu_settings():
-    if torch.cuda.is_available():
-        # GPU 메모리 관리 최적화
-        torch.backends.cuda.matmul.allow_tf32 = True
-        torch.backends.cudnn.benchmark = True
-        torch.backends.cudnn.enabled = True
-        torch.backends.cudnn.deterministic = False
-        
-        # L40S에 최적화된 메모리 설정
-        torch.cuda.empty_cache()
-        torch.cuda.set_device(0)
-        
-        # CUDA 스트림 최적화
-        torch.cuda.Stream(0)
-        
-        # 메모리 할당 최적화
-        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")
-        
-        # L40S 특화 설정
-        if 'L40S' in torch.cuda.get_device_name(0):
-            torch.cuda.set_per_process_memory_fraction(0.95)
-
-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': []
-    }
-    last_section = None
-
-    # 마지막 섹션 태그 찾기
-    for i, line in enumerate(lines):
-        if '[verse]' in line.lower() or '[chorus]' in line.lower() or '[bridge]' in line.lower():
-            last_section = i
-
-    for i, line in enumerate(lines):
-        lower_line = line.lower()
-        
-        # 섹션 태그 처리
-        if '[verse]' in lower_line:
-            if current_section:  # 이전 섹션의 라인들 저장
-                section_lines[current_section].extend(lines[last_section_start:i])
-            current_section = 'verse'
-            sections['verse'] += 1
-            last_section_start = i + 1
-            continue
-        elif '[chorus]' in lower_line:
-            if current_section:
-                section_lines[current_section].extend(lines[last_section_start:i])
-            current_section = 'chorus'
-            sections['chorus'] += 1
-            last_section_start = i + 1
-            continue
-        elif '[bridge]' in lower_line:
-            if current_section:
-                section_lines[current_section].extend(lines[last_section_start:i])
-            current_section = 'bridge'
-            sections['bridge'] += 1
-            last_section_start = i + 1
-            continue
-
-    # 마지막 섹션의 라인들 추가
-    if current_section 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 = section_lines['chorus'][:]
-        for _ in range(repeat_chorus - 1):
-            section_lines['chorus'].extend(original_chorus)
-
-    # 섹션별 라인 수 확인 로깅
-    logging.info(f"Section line counts - Verse: {len(section_lines['verse'])}, "
-                f"Chorus: {len(section_lines['chorus'])}, "
-                f"Bridge: {len(section_lines['bridge'])}")
-
-    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)
-    
-    # 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 (마지막 섹션 포함)
-    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]
-    
-    # 전체 시간 계산 (여유 시간 추가)
-    total_duration = sum(duration for duration in section_durations.values())
-    total_duration = max(60, int(total_duration * 1.2))  # 20% 여유 시간 추가
-    
-    # 토큰 계산 (마지막 섹션을 위한 추가 토큰)
-    base_tokens = 3000
-    tokens_per_line = 200
-    extra_tokens = 1000  # 마지막 섹션을 위한 추가 토큰
-    
-    total_tokens = base_tokens + (total_lines * tokens_per_line) + extra_tokens
-    
-    # 세그먼트 수 계산 (마지막 섹션을 ��한 추가 세그먼트)
-    if sections['chorus'] > 0:
-        num_segments = 4  # 코러스가 있는 경우 4개 세그먼트
-    else:
-        num_segments = 3  # 코러스가 없는 경우 3개 세그먼트
-    
-    # 토큰 수 제한 (더 큰 제한)
-    max_tokens = min(12000, total_tokens)  # 최대 토큰 수 증가
-    
-    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 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 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 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 infer(genre_txt_content, lyrics_txt_content, num_segments, max_new_tokens):
-    genre_txt_path = None
-    lyrics_txt_path = None
-    
-    try:
-        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}")
-        
-        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_")
-        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)
-
-        # 수정된 command - 지원되지 않는 인수 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", "16",
-            "--output_dir", output_dir,
-            "--cuda_idx", "0",
-            "--max_new_tokens", str(actual_max_tokens),
-            "--disable_offload_model"  # GPU 메모리 최적화를 위해 추가
-        ]
-
-        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
-        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}")
-
-def optimize_model_selection(lyrics, genre):
-    model_path = detect_and_select_model(lyrics)
-    params = calculate_generation_params(lyrics)
-    
-    has_chorus = params['sections']['chorus'] > 0
-    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": 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 main():
-    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")
-
-            gr.Examples(
-                examples=[
-                    [
-                        "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"""
-                    ],
-                    [
-                        "K-pop bright energetic synth dance electronic",
-                        """[verse]
-언젠가 마주한 눈빛 속에서
-
-[chorus]
-다시 한 번 내게 말해줘
-
-[verse]
-어두운 밤을 지날 때마다
-
-[chorus]
-다시 한 번 내게 말해줘
-"""
-                    ]
-                ],
-                inputs=[genre_txt, lyrics_txt]
-            )
-
-        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)"
-            )
-
-        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=8
-    )