AudioCraft Custom 프로젝트 완전 분석: AI 오디오 생성의 최첨단 기술

# audiocraft/models/musicgen.py
class MusicGen(BaseGenModel):
    """MusicGen main model with convenient generation API.

    Args:
        name (str): name of the model.
        compression_model (CompressionModel): Compression model
            used to map audio to invertible discrete representations.
        lm (LMModel): Language model over discrete representations.
        max_duration (float, optional): maximum duration the model can produce,
            otherwise, inferred from the training params.
    """
    def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel,
                 max_duration: tp.Optional[float] = None, **kwargs):
        super().__init__(name, compression_model, lm, max_duration, **kwargs)

    @staticmethod
    def get_pretrained(name: str = 'facebook/musicgen-medium', device=None):
        """Return pretrained model, we provide four models:
        - facebook/musicgen-small (300M), text to music,
          # see: https://huggingface.co/facebook/musicgen-small
        - facebook/musicgen-medium (1.5B), text to music,
          # see: https://huggingface.co/facebook/musicgen-medium  
        - facebook/musicgen-melody (1.5B) text to music and text+melody to music,
          # see: https://huggingface.co/facebook/musicgen-melody
        - facebook/musicgen-large (3.3B), text to music,
          # see: https://huggingface.co/facebook/musicgen-large
        """

# audiocraft/models/audiogen.py
class AudioGen(MusicGen):
    """AudioGen model for text-to-sound generation.
    This is a thin wrapper around MusicGen as both models have the same architecture.
    """
    
    def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel,
                 max_duration: tp.Optional[float] = None, **kwargs):
        super().__init__(name, compression_model, lm, max_duration, **kwargs)

    @staticmethod  
    def get_pretrained(name: str = 'facebook/audiogen-medium', device=None):
        """Return pretrained AudioGen model."""

# audiocraft/models/encodec.py
class CompressionModel(nn.Module):
    """Base class for all compression model (e.g, EnCodec, AudioMAE, DAC etc.).
    
    Args:
        sample_rate (int): Sample rate of the audio.
        channels (int): Number of audio channels.
        normalize (bool): Whether to normalize the audio.
        segment (float, optional): Segment length for processing.
        overlap (float, optional): Overlap between segments.
    """
    
    def encode(self, x: torch.Tensor) -> tp.List[EncodedFrame]:
        """Encode audio into discrete tokens."""
        
    def decode(self, encoded_frames: tp.List[EncodedFrame]) -> torch.Tensor:
        """Decode tokens back to audio."""

# audiocraft/adversarial/discriminators/mpd.py
class PeriodDiscriminator(nn.Module):
    """Period sub-discriminator.

    Args:
        period (int): Period between samples of audio.
        in_channels (int): Number of input channels.
        out_channels (int): Number of output channels.
        n_layers (int): Number of convolutional layers.
        kernel_sizes (list of int): Kernel sizes for convolutions.
        stride (int): Stride for convolutions.
        filters (int): Initial number of filters in convolutions.
        filters_scale (int): Multiplier of number of filters as we increase depth.
        max_filters (int): Maximum number of filters.
    """
    def __init__(self, period: int, in_channels: int = 1, out_channels: int = 1,
                 n_layers: int = 5, kernel_sizes: tp.List[int] = [5, 3], stride: int = 3,
                 filters: int = 8, filters_scale: int = 4, max_filters: int = 1024,
                 norm: str = 'weight_norm', activation: str = 'LeakyReLU',
                 activation_params: dict = {'negative_slope': 0.2}):
        super().__init__()
        self.period = period
        self.n_layers = n_layers
        self.activation = getattr(torch.nn, activation)(**activation_params)
        self.convs = nn.ModuleList()
        
    def forward(self, x):
        """Forward pass through period discriminator."""
        # Reshape input according to period
        b, c, t = x.shape
        if t % self.period != 0:
            n_pad = self.period - (t % self.period)
            x = F.pad(x, (0, n_pad), "reflect")
            t = t + n_pad
        x = x.view(b, c, t // self.period, self.period)
        
        fmap = []
        for conv in self.convs:
            x = conv(x)
            x = self.activation(x)
            fmap.append(x)
            
        return x, fmap

class MultiPeriodDiscriminator(MultiDiscriminator):
    """Multi-Period Discriminator (MPD) from HiFi-GAN."""
    
    def __init__(self, periods: tp.List[int] = [2, 3, 5, 7, 11], **kwargs):
        discriminators = [PeriodDiscriminator(p, **kwargs) for p in periods]
        super().__init__(discriminators)

# audiocraft/adversarial/discriminators/msd.py
class ScaleDiscriminator(nn.Module):
    """Scale sub-discriminator."""
    
    def __init__(self, norm: str = 'spectral_norm', **kwargs):
        super().__init__()
        self.norm = norm
        self.convs = nn.ModuleList([
            NormConv1d(1, 128, 15, 1, padding=7),
            NormConv1d(128, 128, 41, 2, groups=4, padding=20),
            NormConv1d(128, 256, 41, 2, groups=16, padding=20),
            NormConv1d(256, 512, 41, 4, groups=16, padding=20),
            NormConv1d(512, 1024, 41, 4, groups=16, padding=20),
            NormConv1d(1024, 1024, 41, 1, groups=16, padding=20),
            NormConv1d(1024, 1024, 5, 1, padding=2),
        ])
        self.conv_post = NormConv1d(1024, 1, 3, 1, padding=1)

class MultiScaleDiscriminator(MultiDiscriminator):
    """Multi-Scale Discriminator (MSD) from HiFi-GAN."""
    
    def __init__(self, scales: tp.List[int] = [1, 2, 4], **kwargs):
        discriminators = []
        for scale in scales:
            discriminators.append(ScaleDiscriminator(**kwargs))
        super().__init__(discriminators, pools=[nn.AvgPool1d(scale * 2, scale, padding=scale) 
                                              if scale > 1 else nn.Identity() for scale in scales])

# audiocraft/adversarial/discriminators/msstftd.py
class STFTDiscriminator(nn.Module):
    """STFT sub-discriminator."""
    
    def __init__(self, n_fft: int = 1024, hop_length: int = 256, win_length: int = 1024, **kwargs):
        super().__init__()
        self.n_fft = n_fft
        self.hop_length = hop_length
        self.win_length = win_length
        self.register_buffer('window', torch.hann_window(win_length))
        
    def forward(self, x):
        """Apply STFT and discriminate in frequency domain."""
        x = torch.stft(x.squeeze(1), n_fft=self.n_fft, hop_length=self.hop_length,
                      win_length=self.win_length, window=self.window, return_complex=True)
        x = torch.view_as_real(x)
        x = rearrange(x, 'b f t c -> b c f t')
        
        # Apply 2D convolutions in time-frequency domain
        fmap = []
        for conv in self.convs:
            x = conv(x)
            x = self.activation(x)
            fmap.append(x)
            
        return x, fmap

class MultiScaleSTFTDiscriminator(MultiDiscriminator):
    """Multi-Scale STFT Discriminator for frequency domain analysis."""
    
    def __init__(self, n_ffts: tp.List[int] = [1024, 2048, 4096], 
                 hop_lengths: tp.List[int] = [120, 240, 480], **kwargs):
        discriminators = [STFTDiscriminator(n_fft, hop_length, **kwargs) 
                         for n_fft, hop_length in zip(n_ffts, hop_lengths)]
        super().__init__(discriminators)

# api/main.py
from fastapi import FastAPI, HTTPException, UploadFile, File, Form
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
from pydantic import BaseModel

app = FastAPI(
    title="AudioCraft API",
    description="AudioCraft의 모든 모델을 REST API로 제공하는 서비스",
    version="1.0.0"
)

# 모델 초기화
models = {
    "musicgen": MusicGen.get_pretrained("facebook/musicgen-small"),
    "audiogen": AudioGen.get_pretrained("facebook/audiogen-medium"),
    "encodec": EncodecModel.get_pretrained("facebook/encodec_24khz"),
    "multiband": MultiBandDiffusion.get_pretrained("facebook/multiband-diffusion")
}

# 판별자 초기화
discriminators = {
    "mpd": MultiPeriodDiscriminator(periods=[2, 3, 5, 7, 11], channels=32, kernel_size=5),
    "msd": MultiScaleDiscriminator(scales=[1, 2, 4], channels=32, kernel_size=5),
    "msstftd": MultiScaleSTFTDiscriminator(n_ffts=[1024, 2048, 4096], hop_lengths=[120, 240, 480], channels=32)
}

class TextToAudioRequest(BaseModel):
    """텍스트-오디오 생성 요청 모델"""
    text: str
    duration: float = 10.0
    temperature: float = 1.0
    top_k: int = 250
    top_p: float = 0.0
    cfg_coef: float = 3.0

@app.post("/generate/music", response_class=FileResponse)
async def generate_music(request: TextToAudioRequest):
    """
    텍스트 프롬프트를 사용하여 음악을 생성합니다.
    """
    try:
        model = models["musicgen"]
        model.set_generation_params(
            duration=request.duration,
            temperature=request.temperature,
            top_k=request.top_k,
            top_p=request.top_p,
            cfg_coef=request.cfg_coef
        )
        
        wav = model.generate([request.text])
        
        # 임시 파일로 저장
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
            torchaudio.save(tmp.name, wav.cpu(), 32000)
            return tmp.name
            
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"음악 생성 중 오류 발생: {str(e)}")

class AudioAnalysisResponse(BaseModel):
    """오디오 분석 결과를 위한 응답 모델"""
    mpd_score: float
    msd_score: float
    msstftd_score: float
    feature_maps: List[List[float]]
    is_real: bool

@app.post("/analyze", response_model=AudioAnalysisResponse)
async def analyze_audio(
    audio_file: UploadFile = File(...),
    threshold: float = 0.5
):
    """
    오디오 파일을 분석하여 각 판별자의 결과를 반환합니다.
    """
    try:
        audio_data = await audio_file.read()
        waveform = process_audio(audio_data)
        
        with torch.no_grad():
            # MPD 분석
            mpd_logits, mpd_features = discriminators["mpd"](waveform)
            mpd_score = torch.mean(torch.sigmoid(mpd_logits[0])).item()
            
            # MSD 분석
            msd_logits, msd_features = discriminators["msd"](waveform)
            msd_score = torch.mean(torch.sigmoid(msd_logits[0])).item()
            
            # MS-STFT-D 분석
            msstftd_logits, msstftd_features = discriminators["msstftd"](waveform)
            msstftd_score = torch.mean(torch.sigmoid(msstftd_logits[0])).item()
            
            # 특징 맵 추출
            feature_maps = []
            for features in [mpd_features, msd_features, msstftd_features]:
                for feat in features:
                    feature_maps.append(feat.mean(dim=1).cpu().numpy().tolist())
        
        is_real = (mpd_score + msd_score + msstftd_score) / 3 > threshold
        
        return AudioAnalysisResponse(
            mpd_score=mpd_score,
            msd_score=msd_score,
            msstftd_score=msstftd_score,
            feature_maps=feature_maps,
            is_real=is_real
        )
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"분석 중 오류 발생: {str(e)}")

# demos/musicgen_demo.ipynb
from audiocraft.models import MusicGen
from audiocraft.models import MultiBandDiffusion

USE_DIFFUSION_DECODER = False
# Using small model, better results would be obtained with `medium` or `large`.
model = MusicGen.get_pretrained('facebook/musicgen-small')
if USE_DIFFUSION_DECODER:
    mbd = MultiBandDiffusion.get_mbd_musicgen()

# 생성 파라미터 설정
model.set_generation_params(
    use_sampling=True,
    top_k=250,
    duration=30
)

# 텍스트 조건부 생성
output = model.generate(
    descriptions=[
        '80s pop track with bassy drums and synth',
        '90s rock song with loud guitars and heavy drums',
        'Progressive rock drum and bass solo',
        'Punk Rock song with loud drum and power guitar',
        'Bluesy guitar instrumental with soulful licks and a driving rhythm section',
        'Jazz Funk song with slap bass and powerful saxophone',
    ],
    progress=True
)
display_audio(output, sample_rate=32000)

# 기존 오디오를 기반으로 연속 생성
import torchaudio
prompt_waveform, prompt_sr = torchaudio.load("../assets/bach.mp3")
prompt_duration = 2
prompt_waveform = prompt_waveform[..., :int(prompt_duration * prompt_sr)]

output = model.generate_continuation(
    prompt_waveform, 
    prompt_sample_rate=prompt_sr, 
    progress=True, 
    return_tokens=True
)
display_audio(output[0], sample_rate=32000)

if USE_DIFFUSION_DECODER:
    out_diffusion = mbd.tokens_to_wav(output[1])
    display_audio(out_diffusion, sample_rate=32000)

# 모델 로딩 최적화
@lru_cache(maxsize=None)
def load_model_cached(model_name: str):
    """캐시된 모델 로딩으로 메모리 효율성 향상"""
    if model_name == "musicgen":
        return MusicGen.get_pretrained("facebook/musicgen-small")
    elif model_name == "audiogen":
        return AudioGen.get_pretrained("facebook/audiogen-medium")
    # ...

# GPU 메모리 관리
def manage_gpu_memory():
    """GPU 메모리 정리"""
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        torch.cuda.synchronize()

def batch_generate_music(prompts: List[str], batch_size: int = 4):
    """배치 단위 음악 생성으로 처리량 향상"""
    results = []
    for i in range(0, len(prompts), batch_size):
        batch = prompts[i:i+batch_size]
        with torch.no_grad():
            output = model.generate(batch)
            results.extend(output)
    return results

import asyncio
from concurrent.futures import ThreadPoolExecutor

async def async_generate_audio(request: TextToAudioRequest):
    """비동기 오디오 생성"""
    loop = asyncio.get_event_loop()
    with ThreadPoolExecutor() as executor:
        result = await loop.run_in_executor(
            executor, 
            lambda: model.generate([request.text])
        )
    return result

# 음악 제작을 위한 고급 워크플로우
class MusicProductionPipeline:
    def __init__(self):
        self.musicgen = MusicGen.get_pretrained('facebook/musicgen-large')
        self.audiogen = AudioGen.get_pretrained('facebook/audiogen-medium')
        self.encodec = EncodecModel.get_pretrained('facebook/encodec_24khz')
        
    def create_song_structure(self, sections: Dict[str, str]):
        """섹션별 음악 생성"""
        results = {}
        for section, prompt in sections.items():
            results[section] = self.musicgen.generate([prompt])
        return results
        
    def add_sound_effects(self, base_audio: torch.Tensor, effects: List[str]):
        """사운드 이펙트 추가"""
        fx_audio = []
        for effect in effects:
            fx = self.audiogen.generate([effect])
            fx_audio.append(fx)
        return self.mix_audio(base_audio, fx_audio)
        
    def compress_for_distribution(self, audio: torch.Tensor):
        """배포용 압축"""
        codes = self.encodec.encode(audio)
        compressed = self.encodec.decode(codes)
        return compressed

class GameAudioSystem:
    """게임용 동적 오디오 생성 시스템"""
    
    def __init__(self):
        self.audiogen = AudioGen.get_pretrained('facebook/audiogen-medium')
        self.sound_cache = {}
        
    def generate_ambient_sound(self, environment: str):
        """환경별 배경음 생성"""
        prompts = {
            "forest": "gentle forest ambiance with birds chirping and leaves rustling",
            "ocean": "calm ocean waves with seagull sounds",
            "city": "urban city ambiance with distant traffic and footsteps",
            "dungeon": "dark dungeon atmosphere with water drops and wind"
        }
        
        if environment not in self.sound_cache:
            audio = self.audiogen.generate([prompts[environment]])
            self.sound_cache[environment] = audio
            
        return self.sound_cache[environment]
        
    def generate_dynamic_music(self, game_state: Dict):
        """게임 상태에 따른 동적 음악"""
        tension = game_state.get('tension', 0.5)
        location = game_state.get('location', 'neutral')
        
        if tension > 0.8:
            prompt = f"intense battle music for {location} with dramatic orchestration"
        elif tension > 0.5:
            prompt = f"suspenseful {location} music with building tension"
        else:
            prompt = f"peaceful {location} ambient music"
            
        return self.musicgen.generate([prompt])

class PodcastAudioProcessor:
    """팟캐스트 제작 자동화"""
    
    def create_intro_music(self, podcast_theme: str):
        """팟캐스트 인트로 음악 생성"""
        prompt = f"upbeat podcast intro music for {podcast_theme} show, 15 seconds"
        return self.musicgen.generate([prompt])
        
    def generate_transition_sounds(self, count: int = 5):
        """전환 사운드 생성"""
        transitions = []
        prompts = [
            "smooth podcast transition sound",
            "gentle chime for section break",
            "soft whoosh transition effect",
            "minimalist transition tone",
            "clean section divider sound"
        ]
        
        for prompt in prompts[:count]:
            sound = self.audiogen.generate([prompt])
            transitions.append(sound)
            
        return transitions

# 미래 개선 방향 (예시)
class NextGenAudioCraft:
    """차세대 AudioCraft 시스템"""
    
    def __init__(self):
        self.streaming_generator = StreamingMusicGen()
        self.fine_control = FinegrainedController()
        self.quality_enhancer = AudioQualityEnhancer()
        
    def real_time_generation(self, prompt: str):
        """실시간 스트리밍 생성"""
        # 청크 단위 실시간 생성
        for chunk in self.streaming_generator.generate_stream(prompt):
            yield chunk
            
    def style_transfer(self, content_audio: torch.Tensor, style_prompt: str):
        """오디오 스타일 전송"""
        # 기존 오디오의 스타일 변경
        return self.fine_control.transfer_style(content_audio, style_prompt)
        
    def adaptive_quality(self, audio: torch.Tensor, target_quality: str):
        """적응적 품질 향상"""
        # 사용 목적에 따른 품질 최적화
        return self.quality_enhancer.enhance(audio, target_quality)