import spaces import logging from datetime import datetime from pathlib import Path import gradio as gr import torch import torchaudio import os import requests from transformers import pipeline import tempfile import numpy as np from einops import rearrange import cv2 from scipy.io import wavfile import librosa import json from typing import Optional, Tuple, List import atexit try: import mmaudio except ImportError: os.system("pip install -e .") import mmaudio from mmaudio.eval_utils import (ModelConfig, all_model_cfg, generate, load_video, make_video, setup_eval_logging) from mmaudio.model.flow_matching import FlowMatching from mmaudio.model.networks import MMAudio, get_my_mmaudio from mmaudio.model.sequence_config import SequenceConfig from mmaudio.model.utils.features_utils import FeaturesUtils # 로깅 설정 logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) log = logging.getLogger() # CUDA 설정 if torch.cuda.is_available(): device = torch.device("cuda") torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True torch.backends.cudnn.benchmark = True else: device = torch.device("cpu") dtype = torch.bfloat16 # 모델 설정 model: ModelConfig = all_model_cfg['large_44k_v2'] model.download_if_needed() output_dir = Path('./output/gradio') setup_eval_logging() # 번역기 및 Pixabay API 설정 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device="cpu") PIXABAY_API_KEY = "33492762-a28a596ec4f286f84cd328b17" # CSS 스타일 정의 custom_css = """ .gradio-container { background: linear-gradient(45deg, #1a1a1a, #2a2a2a); border-radius: 15px; box-shadow: 0 8px 32px rgba(0,0,0,0.3); } .input-container, .output-container { background: rgba(255,255,255,0.1); backdrop-filter: blur(10px); border-radius: 10px; padding: 20px; transform-style: preserve-3d; transition: transform 0.3s ease; } .input-container:hover { transform: translateZ(20px); } .gallery-item { transition: transform 0.3s ease; border-radius: 8px; overflow: hidden; } .gallery-item:hover { transform: scale(1.05); box-shadow: 0 4px 15px rgba(0,0,0,0.2); } .tabs { background: rgba(255,255,255,0.05); border-radius: 10px; padding: 10px; } button { background: linear-gradient(45deg, #4a90e2, #357abd); border: none; border-radius: 5px; transition: all 0.3s ease; } button:hover { transform: translateY(-2px); box-shadow: 0 4px 15px rgba(74,144,226,0.3); } """ def cleanup_temp_files(): temp_dir = tempfile.gettempdir() for file in os.listdir(temp_dir): if file.endswith(('.mp4', '.flac')): try: os.remove(os.path.join(temp_dir, file)) except: pass atexit.register(cleanup_temp_files) def get_model() -> tuple[MMAudio, FeaturesUtils, SequenceConfig]: with torch.cuda.device(device): seq_cfg = model.seq_cfg net: MMAudio = get_my_mmaudio(model.model_name).to(device, dtype).eval() net.load_weights(torch.load(model.model_path, map_location=device, weights_only=True)) log.info(f'Loaded weights from {model.model_path}') feature_utils = FeaturesUtils( tod_vae_ckpt=model.vae_path, synchformer_ckpt=model.synchformer_ckpt, enable_conditions=True, mode=model.mode, bigvgan_vocoder_ckpt=model.bigvgan_16k_path, need_vae_encoder=False ).to(device, dtype).eval() return net, feature_utils, seq_cfg net, feature_utils, seq_cfg = get_model() # search_videos 함수 수정 @torch.no_grad() def search_videos(query): try: # CPU에서 번역 실행 query = translate_prompt(query) return search_pixabay_videos(query, PIXABAY_API_KEY) except Exception as e: logging.error(f"Video search error: {e}") return [] # translate_prompt 함수도 수정 def translate_prompt(text): try: if text and any(ord(char) >= 0x3131 and ord(char) <= 0xD7A3 for char in text): # CPU에서 번역 실행 with torch.no_grad(): translation = translator(text)[0]['translation_text'] return translation return text except Exception as e: logging.error(f"Translation error: {e}") return text # 디바이스 설정 부분 수정 if torch.cuda.is_available(): device = torch.device("cuda") torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True torch.backends.cudnn.benchmark = True else: device = torch.device("cpu") # 번역기 설정 수정 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device="cpu") # 명시적으로 CPU 지정 def search_pixabay_videos(query, api_key): try: base_url = "https://pixabay.com/api/videos/" params = { "key": api_key, "q": query, "per_page": 80 } response = requests.get(base_url, params=params) if response.status_code == 200: data = response.json() return [video['videos']['large']['url'] for video in data.get('hits', [])] return [] except Exception as e: logging.error(f"Pixabay API error: {e}") return [] @spaces.GPU @torch.inference_mode() def video_to_audio(video: gr.Video, prompt: str, negative_prompt: str, seed: int, num_steps: int, cfg_strength: float, duration: float): prompt = translate_prompt(prompt) negative_prompt = translate_prompt(negative_prompt) rng = torch.Generator(device=device) rng.manual_seed(seed) fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps) clip_frames, sync_frames, duration = load_video(video, duration) clip_frames = clip_frames.unsqueeze(0) sync_frames = sync_frames.unsqueeze(0) seq_cfg.duration = duration net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len) audios = generate(clip_frames, sync_frames, [prompt], negative_text=[negative_prompt], feature_utils=feature_utils, net=net, fm=fm, rng=rng, cfg_strength=cfg_strength) audio = audios.float().cpu()[0] video_save_path = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4').name make_video(video, video_save_path, audio, sampling_rate=seq_cfg.sampling_rate, duration_sec=seq_cfg.duration) return video_save_path @spaces.GPU @torch.inference_mode() def text_to_audio(prompt: str, negative_prompt: str, seed: int, num_steps: int, cfg_strength: float, duration: float): prompt = translate_prompt(prompt) negative_prompt = translate_prompt(negative_prompt) rng = torch.Generator(device=device) rng.manual_seed(seed) fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps) clip_frames = sync_frames = None seq_cfg.duration = duration net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len) audios = generate(clip_frames, sync_frames, [prompt], negative_text=[negative_prompt], feature_utils=feature_utils, net=net, fm=fm, rng=rng, cfg_strength=cfg_strength) audio = audios.float().cpu()[0] audio_save_path = tempfile.NamedTemporaryFile(delete=False, suffix='.flac').name torchaudio.save(audio_save_path, audio, seq_cfg.sampling_rate) return audio_save_path # 인터페이스 정의 video_search_tab = gr.Interface( fn=search_videos, inputs=gr.Textbox(label="검색어 입력"), outputs=gr.Gallery(label="검색 결과", columns=4, rows=20), css=custom_css, api_name=False ) video_to_audio_tab = gr.Interface( fn=video_to_audio, inputs=[ gr.Video(label="비디오"), gr.Textbox(label="프롬프트"), gr.Textbox(label="네거티브 프롬프트", value="music"), gr.Number(label="시드", value=0), gr.Number(label="스텝 수", value=25), gr.Number(label="가이드 강도", value=4.5), gr.Number(label="길이(초)", value=8), ], outputs="playable_video", css=custom_css ) text_to_audio_tab = gr.Interface( fn=text_to_audio, inputs=[ gr.Textbox(label="프롬프트"), gr.Textbox(label="네거티브 프롬프트"), gr.Number(label="시드", value=0), gr.Number(label="스텝 수", value=25), gr.Number(label="가이드 강도", value=4.5), gr.Number(label="길이(초)", value=8), ], outputs="audio", css=custom_css ) # 메인 실행 if __name__ == "__main__": gr.TabbedInterface( [video_search_tab, video_to_audio_tab, text_to_audio_tab], ["비디오 검색", "비디오-오디오 변환", "텍스트-오디오 변환"], css=custom_css ).launch(allowed_paths=[output_dir])