Create app-backup.py

app-backup.py

@@ -0,0 +1,548 @@
+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
+    )