app.py

import spaces
import logging
from datetime import datetime
from pathlib import Path
import gradio as gr
import torch
import torchaudio
import os
from transformers import pipeline
from pixabay import Image, Video
import tempfile

# 기본 설정
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

# CUDA 설정
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True

# 로깅 설정
log = logging.getLogger()

# 장치 및 데이터 타입 설정
device = 'cuda'
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")



import requests

def search_pixabay_videos(query, api_key):
    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 []

# 메인 코드에서 pixabay 관련 부분 수정
PIXABAY_API_KEY = "33492762-a28a596ec4f286f84cd328b17"

def search_videos(query):
    query = translate_prompt(query)
    return search_pixabay_videos(query, PIXABAY_API_KEY)



# 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 get_model() -> tuple[MMAudio, FeaturesUtils, SequenceConfig]:
    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)
    feature_utils = feature_utils.to(device, dtype).eval()

    return net, feature_utils, seq_cfg

net, feature_utils, seq_cfg = get_model()

def translate_prompt(text):
    if text and any(ord(char) >= 0x3131 and ord(char) <= 0xD7A3 for char in text):
        translation = translator(text)[0]['translation_text']
        return translation
    return text

def search_videos(query):
    query = translate_prompt(query)
    videos = pixabay_video.search(q=query, per_page=80)
    return [video.video_large for video in videos['hits']]

@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
)

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])