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 initialize_system() # samples 디렉토리 및 예제 파일 처리 current_dir = os.path.dirname(os.path.abspath(__file__)) samples_dir = os.path.join(current_dir, 'samples') sample_audio_path = os.path.join(samples_dir, 'metal.mp3') os.makedirs(samples_dir, exist_ok=True) 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("""

Open SUNO: Full-Song Generation (Multi-Language Support)

Enter your song details below and let the AI handle the music production!

""") # 예제 음악 섹션 with gr.Group(): gr.HTML("""

Sample Generated Music

Listen to this example

""") if os.path.exists(sample_audio_path): gr.Audio( value=sample_audio_path, label="Sample Music", type="filepath" ) else: gr.Markdown("### Sample music file not available") 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(): 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 ) 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") 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 )