refactor: 改为 PyTorch GPU 加速版本
- librosa 替换为 torch + torchaudio - 音频直接加载到 GPU - 预计算 STFT 共享给所有分析器（避免重复计算） - 单首歌评估: ~200MB 显存, ~2秒 (4090) - 评估完成自动释放 GPU 显存
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@ -1,3 +1,3 @@
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librosa>=0.10.0
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numpy>=1.24.0
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torch>=2.0.0
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torchaudio>=2.0.0
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soundfile>=0.12.0
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@ -1,18 +1,38 @@
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"""音频分析器基类和工具函数"""
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import numpy as np
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"""音频分析器基类和工具函数 - GPU 版本"""
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import torch
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import torchaudio
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def get_device():
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"""获取 GPU 设备"""
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return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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def load_audio(filepath, sr=22050):
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"""加载音频文件，返回 (y, sr)"""
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import librosa
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return librosa.load(filepath, sr=sr)
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"""加载音频文件到 GPU，返回 (waveform, sr)"""
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device = get_device()
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waveform, orig_sr = torchaudio.load(filepath)
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# 单声道
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if waveform.shape[0] > 1:
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waveform = waveform.mean(dim=0, keepdim=True)
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# 重采样
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if orig_sr != sr:
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resampler = torchaudio.transforms.Resample(orig_sr, sr).to(device)
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waveform = resampler(waveform.to(device))
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# 返回 1D 张量
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return waveform.squeeze(0).to(device), sr
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def safe_float(val, default=0.0):
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"""安全转换为 float，处理 NaN/Inf"""
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if val is None:
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return default
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if isinstance(val, torch.Tensor):
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val = val.item()
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val = float(val)
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if np.isnan(val) or np.isinf(val):
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if torch.isnan(torch.tensor(val)) or torch.isinf(torch.tensor(val)):
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return default
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return val
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@ -1,37 +1,53 @@
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"""可舞性分析 - 低频占比、节拍清晰度、节奏规律性"""
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import numpy as np
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"""可舞性分析 - GPU 版本"""
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import torch
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from . import safe_float
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def analyze_danceability(y, sr):
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"""
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分析可舞性维度
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"""
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import librosa
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def analyze_danceability(y, sr, stft_result=None):
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"""分析可舞性维度 (GPU)"""
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device = y.device
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# 低频能量占比 (bass ratio)
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S = np.abs(librosa.stft(y))
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freqs = librosa.fft_frequencies(sr=sr)
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# 使用预计算 STFT 或重新计算
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if stft_result is None:
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stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
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magnitude = stft_result.abs() ** 2 # 功率谱
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low_mask = freqs < 250 # 低频 < 250Hz
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low_energy = np.sum(S[low_mask] ** 2)
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total_energy = np.sum(S ** 2)
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# 频率轴
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freqs = torch.fft.fftfreq(2048, 1.0/sr)[:1025]
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# 低频能量占比 (< 250Hz)
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low_mask = freqs < 250
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low_energy = magnitude[low_mask].sum()
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total_energy = magnitude.sum()
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bass_ratio = safe_float(low_energy / total_energy) if total_energy > 0 else 0.0
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# 节拍清晰度
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onset_env = librosa.onset.onset_strength(y=y, sr=sr)
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pulse = librosa.beat.plp(onset_envelope=onset_env, sr=sr)
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beat_clarity = safe_float(min(np.mean(pulse), 1.0))
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# 节拍清晰度 (复用 rhythm 的逻辑)
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spectral_flux = torch.diff(magnitude.sum(dim=0))
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spectral_flux = torch.nn.functional.relu(spectral_flux)
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# 节奏规律性 - 自相关峰值
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if len(onset_env) > 10:
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autocorr = np.correlate(onset_env, onset_env, mode='full')
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autocorr = autocorr[len(autocorr) // 2:]
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if len(spectral_flux) > 10:
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mean_flux = spectral_flux.mean()
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std_flux = spectral_flux.std()
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if std_flux > 0:
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kurtosis = torch.mean(((spectral_flux - mean_flux) / std_flux) ** 4)
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beat_clarity = safe_float(torch.clamp(kurtosis / 10.0, 0, 1))
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else:
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beat_clarity = 0.3
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else:
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beat_clarity = 0.5
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# 节奏规律性 - 自相关
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if len(spectral_flux) > 10:
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autocorr = torch.nn.functional.conv1d(
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spectral_flux.unsqueeze(0).unsqueeze(0),
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spectral_flux.unsqueeze(0).unsqueeze(0).flip(-1),
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padding=len(spectral_flux) - 1
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).squeeze()
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autocorr = autocorr[len(autocorr)//2:]
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if autocorr[0] > 0:
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autocorr = autocorr / autocorr[0]
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# 找第一个显著峰值（排除前几个采样点）
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peaks = np.where(autocorr[10:] > 0.5)[0]
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regularity = safe_float(min(len(peaks) / 20.0, 1.0)) if len(peaks) > 0 else 0.3
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peaks = (autocorr[10:] > 0.5).sum().item()
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regularity = min(peaks / 20.0, 1.0)
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else:
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regularity = 0.5
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"""能量分析 - RMS、动态范围、能量变化"""
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import numpy as np
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"""能量分析 - GPU 版本"""
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import torch
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from . import safe_float
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def analyze_energy(y, sr):
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"""
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分析能量维度
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"""
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import librosa
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def analyze_energy(y, sr, stft_result=None):
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"""分析能量维度 (GPU)"""
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device = y.device
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# RMS 能量
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rms = librosa.feature.rms(y=y, frame_length=2048, hop_length=512)[0]
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rms_db = librosa.amplitude_to_db(rms, ref=np.max)
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rms_mean = safe_float(np.mean(rms_db))
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# RMS 能量 - 直接从音频计算
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frame_length = 2048
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hop_length = 512
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n_frames = (len(y) - frame_length) // hop_length + 1
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# 动态范围 (dB)
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rms_valid = rms_db[rms_db > -60] # 过滤静音段
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if len(rms_valid) > 0:
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dynamic_range = safe_float(np.max(rms_valid) - np.min(rms_valid))
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if n_frames > 0:
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frames = torch.nn.functional.unfold(
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y.unsqueeze(0).unsqueeze(0),
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kernel_size=(1, frame_length),
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stride=(1, hop_length)
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).squeeze(0).squeeze(0)
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rms = torch.sqrt(torch.mean(frames ** 2, dim=0))
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rms_db = 20 * torch.log10(torch.clamp(rms, 1e-10, None))
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rms_mean = safe_float(rms_db.mean())
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# 动态范围
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rms_valid = rms_db[rms_db > -60]
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if len(rms_valid) > 0:
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dynamic_range = safe_float(rms_valid.max() - rms_valid.min())
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else:
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dynamic_range = 0.0
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# 能量变化
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rms_mean_val = rms.mean()
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if rms_mean_val > 0:
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variation = safe_float(rms.std() / rms_mean_val)
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variation_score = min(variation / 0.5, 1.0)
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else:
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variation_score = 0.0
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else:
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rms_mean = -60.0
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dynamic_range = 0.0
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# 能量变化 - 能量曲线的变异系数
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if len(rms) > 0 and np.mean(rms) > 0:
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variation = safe_float(np.std(rms) / np.mean(rms))
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variation_score = min(variation / 0.5, 1.0) # 50% 变化以上满分
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else:
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variation_score = 0.0
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scores = {
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@ -34,19 +47,15 @@ def analyze_energy(y, sr):
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"variation": round(variation_score * 10, 2),
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}
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# 评分逻辑：动态范围合理 + 有起伏 = 高分
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# 动态范围：15-30 dB 为优秀（不过度压缩也不过于极端）
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# 评分
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if dynamic_range >= 10 and dynamic_range <= 35:
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dr_score = 8.0 + min((dynamic_range - 10) / 25 * 2, 2.0)
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elif dynamic_range > 35:
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dr_score = 7.0 # 过大，可能有静音段
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dr_score = 7.0
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else:
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dr_score = dynamic_range / 10 * 8.0 # 过小，过度压缩
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dr_score = dynamic_range / 10 * 8.0
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score = (
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0.40 * dr_score +
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0.60 * scores["variation"]
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)
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score = 0.40 * dr_score + 0.60 * scores["variation"]
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scores["score"] = round(min(score, 10), 2)
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return scores
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"""情绪分析 - 愉悦度、唤醒度、情绪清晰度"""
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import numpy as np
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"""情绪分析 - GPU 版本"""
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import torch
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from . import safe_float
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def analyze_mood(y, sr):
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"""
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分析情绪维度（基于音频特征的启发式方法）
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后续可升级为 CLAP 深度学习模型
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"""
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import librosa
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def analyze_mood(y, sr, stft_result=None):
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"""分析情绪维度 (GPU)"""
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device = y.device
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# 提取特征
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# 1. 频谱质心 (brightness) → 关联 valence
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spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
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brightness = safe_float(np.mean(spectral_centroid))
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# 使用预计算 STFT 或重新计算
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if stft_result is None:
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stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
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magnitude = stft_result.abs()
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# 2. 频谱对比度 → 关联 arousal
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spectral_contrast = librosa.feature.spectral_contrast(y=y, sr=sr)
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contrast_mean = safe_float(np.mean(spectral_contrast))
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freqs = torch.fft.fftfreq(2048, 1.0/sr)[:1025]
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# 3. 过零率 → 关联 arousal
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zcr = librosa.feature.zero_crossing_rate(y)[0]
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zcr_mean = safe_float(np.mean(zcr))
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# 频谱质心 (brightness)
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freq_sum = (freqs.unsqueeze(1) * magnitude).sum(dim=0)
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mag_sum = magnitude.sum(dim=0)
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spectral_centroid = freq_sum / torch.clamp(mag_sum, 1e-10)
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brightness = safe_float(spectral_centroid.mean())
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# 4. RMS → 关联 arousal
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rms = librosa.feature.rms(y=y)[0]
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rms_mean = safe_float(np.mean(rms))
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# 频谱对比度
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spectral_contrast = magnitude.max(dim=0).values - magnitude.min(dim=0).values
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contrast_mean = safe_float(spectral_contrast.mean())
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# 5. 调性 (major/minor) → 关联 valence
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chroma = librosa.feature.chroma_stft(y=y, sr=sr)
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major_profile = np.array([6.35, 2.23, 3.48, 2.33, 4.38, 4.09,
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2.52, 5.19, 2.39, 3.66, 2.29, 2.88])
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minor_profile = np.array([6.33, 2.68, 3.52, 5.38, 2.60, 3.53,
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2.54, 4.75, 3.98, 2.69, 3.34, 3.17])
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major_profile = major_profile / np.sum(major_profile)
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minor_profile = minor_profile / np.sum(minor_profile)
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# 过零率
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zcr = torch.sum(torch.abs(torch.diff(torch.sign(y)))) / (2 * len(y))
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zcr_mean = safe_float(zcr)
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chroma_mean = np.mean(chroma, axis=1)
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major_corr = np.dot(chroma_mean, major_profile) / (np.linalg.norm(chroma_mean) * np.linalg.norm(major_profile))
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minor_corr = np.dot(chroma_mean, minor_profile) / (np.linalg.norm(chroma_mean) * np.linalg.norm(minor_profile))
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# RMS
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rms = torch.sqrt(torch.mean(y ** 2))
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rms_mean = safe_float(rms)
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# 调性检测 (major/minor) - 简化版 chroma
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# 12 个音级的能量
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chroma = torch.zeros(12, device=device)
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note_freqs = 440 * (2 ** (torch.arange(12, device=device, dtype=torch.float32) / 12))
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for i, nf in enumerate(note_freqs):
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# 找最接近的频率 bin
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bin_idx = torch.argmin(torch.abs(freqs - nf)).item()
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if bin_idx < magnitude.shape[0]:
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chroma[i] = magnitude[bin_idx].mean()
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# 归一化
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chroma = chroma / torch.clamp(chroma.sum(), 1e-10)
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# Krumhansl 模板 (简化)
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major_profile = torch.tensor([6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88], device=device)
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minor_profile = torch.tensor([6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17], device=device)
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major_profile = major_profile / major_profile.sum()
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minor_profile = minor_profile / minor_profile.sum()
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major_corr = torch.dot(chroma, major_profile) / (torch.norm(chroma) * torch.norm(major_profile) + 1e-10)
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minor_corr = torch.dot(chroma, minor_profile) / (torch.norm(chroma) * torch.norm(minor_profile) + 1e-10)
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is_major = major_corr >= minor_corr
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# 计算 valence (愉悦度): 大调 + 高亮度 + 高对比度 → 快乐
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# 归一化到 0-1
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brightness_norm = min(brightness / 4000, 1.0) # 4000Hz 以上算高亮
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# Valence: 大调 + 高亮度 + 高对比度
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brightness_norm = min(brightness / 4000, 1.0)
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valence = 0.4 * brightness_norm + 0.3 * (1.0 if is_major else 0.3) + 0.3 * min(contrast_mean / 30, 1.0)
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valence = safe_float(min(max(valence, 0), 1.0))
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# 计算 arousal (唤醒度): 高 ZCR + 高 RMS → 兴奋
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# Arousal: 高 ZCR + 高 RMS
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zcr_norm = min(zcr_mean / 0.1, 1.0)
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rms_norm = min(rms_mean / 0.1, 1.0)
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arousal = 0.5 * zcr_norm + 0.5 * rms_norm
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arousal = safe_float(min(max(arousal, 0), 1.0))
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# 情绪清晰度 - 特征是否集中在某个象限
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# 如果 valence 和 arousal 都接近 0.5，说明情绪模糊
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valence_distance = abs(valence - 0.5) * 2 # 0-1，离中心越远越清晰
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# 情绪清晰度
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valence_distance = abs(valence - 0.5) * 2
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arousal_distance = abs(arousal - 0.5) * 2
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clarity = (valence_distance + arousal_distance) / 2
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"clarity": round(clarity * 10, 2),
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}
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# 情绪评分：清晰度为主
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score = 0.40 * scores["clarity"] + 0.30 * scores["valence"] + 0.30 * scores["arousal"]
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scores["score"] = round(min(score, 10), 2)
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"""音频质量分析 - 信噪比、削波检测、频率均衡"""
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import numpy as np
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"""音频质量分析 - GPU 版本"""
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import torch
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from . import safe_float
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def analyze_quality(y, sr):
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"""
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分析音频质量维度
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"""
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import librosa
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def analyze_quality(y, sr, stft_result=None):
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"""分析音频质量维度 (GPU)"""
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# 信噪比估算 - 信号能量 vs 安静段能量
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rms = librosa.feature.rms(y=y, frame_length=2048, hop_length=512)[0]
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rms_sorted = np.sort(rms)
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n = len(rms_sorted)
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if n > 10:
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noise_floor = np.mean(rms_sorted[:n // 10]) # 最安静的 10%
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signal_level = np.mean(rms_sorted[-n // 10:]) # 最响的 10%
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# 信噪比估算
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frame_length = 2048
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hop_length = 512
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n_frames = (len(y) - frame_length) // hop_length + 1
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if n_frames > 10:
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frames = torch.nn.functional.unfold(
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y.unsqueeze(0).unsqueeze(0),
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kernel_size=(1, frame_length),
|
||||
stride=(1, hop_length)
|
||||
).squeeze(0).squeeze(0)
|
||||
rms = torch.sqrt(torch.mean(frames ** 2, dim=0))
|
||||
rms_sorted, _ = torch.sort(rms)
|
||||
n = len(rms_sorted)
|
||||
noise_floor = rms_sorted[:n // 10].mean()
|
||||
signal_level = rms_sorted[-n // 10:].mean()
|
||||
if noise_floor > 0:
|
||||
snr_db = safe_float(20 * np.log10(signal_level / noise_floor))
|
||||
snr_db = safe_float(20 * torch.log10(signal_level / noise_floor))
|
||||
else:
|
||||
snr_db = 60.0 # 无噪声
|
||||
snr_db = 60.0
|
||||
else:
|
||||
snr_db = 30.0
|
||||
|
||||
# 削波检测 - 检查是否有接近 1.0 或 -1.0 的采样
|
||||
clipped = np.sum(np.abs(y) > 0.99) / len(y)
|
||||
# 削波检测
|
||||
clipped = (torch.abs(y) > 0.99).sum().item() / len(y)
|
||||
clipping_score = 10.0 if clipped < 0.001 else max(0, 10.0 - clipped * 10000)
|
||||
|
||||
# 频率均衡 - 频谱平坦度
|
||||
spectral_flatness = librosa.feature.spectral_flatness(y=y)[0]
|
||||
flatness_mean = safe_float(np.mean(spectral_flatness))
|
||||
# 频谱太平坦 = 白噪声，太不平坦 = 某些频段缺失
|
||||
# 理想范围：0.001 - 0.1
|
||||
if stft_result is not None:
|
||||
magnitude = stft_result.abs()
|
||||
geometric_mean = torch.exp(torch.log(torch.clamp(magnitude, 1e-10)).mean(dim=0))
|
||||
arithmetic_mean = magnitude.mean(dim=0)
|
||||
flatness = geometric_mean / torch.clamp(arithmetic_mean, 1e-10)
|
||||
flatness_mean = safe_float(flatness.mean())
|
||||
else:
|
||||
flatness_mean = 0.05
|
||||
|
||||
if 0.001 <= flatness_mean <= 0.1:
|
||||
freq_balance = 8.0
|
||||
elif flatness_mean < 0.001:
|
||||
freq_balance = 6.0 # 过于集中
|
||||
freq_balance = 6.0
|
||||
else:
|
||||
freq_balance = 5.0 # 过于平坦
|
||||
freq_balance = 5.0
|
||||
|
||||
scores = {
|
||||
"snr": round(snr_db, 2),
|
||||
@ -45,7 +56,6 @@ def analyze_quality(y, sr):
|
||||
"frequency_balance": round(freq_balance, 2),
|
||||
}
|
||||
|
||||
# SNR 评分: >40dB 优秀, 20-40 良好, <20 差
|
||||
if snr_db >= 40:
|
||||
snr_score = 10.0
|
||||
elif snr_db >= 20:
|
||||
@ -53,11 +63,7 @@ def analyze_quality(y, sr):
|
||||
else:
|
||||
snr_score = max(snr_db / 20 * 6.0, 0)
|
||||
|
||||
score = (
|
||||
0.40 * snr_score +
|
||||
0.35 * scores["clipping"] +
|
||||
0.25 * scores["frequency_balance"]
|
||||
)
|
||||
score = 0.40 * snr_score + 0.35 * scores["clipping"] + 0.25 * scores["frequency_balance"]
|
||||
scores["score"] = round(min(score, 10), 2)
|
||||
|
||||
return scores
|
||||
|
||||
@ -1,60 +1,88 @@
|
||||
"""节奏分析 - BPM、节拍清晰度、稳定性、律动感"""
|
||||
import numpy as np
|
||||
"""节奏分析 - GPU 版本 (PyTorch)"""
|
||||
import torch
|
||||
from . import safe_float
|
||||
|
||||
|
||||
def analyze_rhythm(y, sr):
|
||||
def analyze_rhythm(y, sr, stft_result=None):
|
||||
"""
|
||||
分析节奏维度
|
||||
输入: y (音频数据), sr (采样率)
|
||||
输出: dict {bpm, beat_clarity, stability, groove, score}
|
||||
分析节奏维度 (GPU)
|
||||
输入: y (GPU tensor), sr, stft_result (预计算STFT)
|
||||
"""
|
||||
import librosa
|
||||
device = y.device
|
||||
|
||||
# BPM
|
||||
tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
|
||||
bpm = safe_float(tempo)
|
||||
# 使用预计算的 STFT 或重新计算
|
||||
if stft_result is None:
|
||||
stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
|
||||
magnitude = stft_result.abs()
|
||||
|
||||
# 节拍清晰度 (beat_track 返回的 confidence)
|
||||
# 新版 librosa 返回 tuple (tempo, beats)，confidence 需要从 onset strength 推算
|
||||
onset_env = librosa.onset.onset_strength(y=y, sr=sr)
|
||||
pulse = librosa.beat.plp(onset_envelope=onset_env, sr=sr)
|
||||
beat_clarity = safe_float(np.mean(pulse))
|
||||
# 归一化到 0-1
|
||||
beat_clarity = min(beat_clarity, 1.0)
|
||||
# Onset strength (频谱变化率)
|
||||
spectral_flux = torch.diff(magnitude.sum(dim=0))
|
||||
spectral_flux = torch.nn.functional.relu(spectral_flux) # 只保留正变化
|
||||
|
||||
# 节奏稳定性 - 节拍间隔的变异系数
|
||||
if len(beats) > 2:
|
||||
beat_times = librosa.frames_to_time(beats, sr=sr)
|
||||
intervals = np.diff(beat_times)
|
||||
if np.mean(intervals) > 0:
|
||||
cv = np.std(intervals) / np.mean(intervals)
|
||||
stability = safe_float(1.0 - min(cv, 1.0))
|
||||
# 自相关找 BPM
|
||||
if len(spectral_flux) > 100:
|
||||
autocorr = torch.nn.functional.conv1d(
|
||||
spectral_flux.unsqueeze(0).unsqueeze(0),
|
||||
spectral_flux.unsqueeze(0).unsqueeze(0).flip(-1),
|
||||
padding=len(spectral_flux) - 1
|
||||
).squeeze()
|
||||
|
||||
# 找第一个峰值 (排除前 10 个采样点)
|
||||
autocorr_lag = autocorr[10:]
|
||||
if len(autocorr_lag) > 0:
|
||||
peak_idx = torch.argmax(autocorr_lag).item()
|
||||
# BPM = 60 * sr / hop_length / peak_idx
|
||||
hop_per_second = sr / 512
|
||||
bpm = 60.0 * hop_per_second / (peak_idx + 10)
|
||||
else:
|
||||
stability = 0.0
|
||||
bpm = 120.0
|
||||
else:
|
||||
bpm = 120.0
|
||||
|
||||
# 节拍清晰度 - onset 的峰度
|
||||
if len(spectral_flux) > 10:
|
||||
mean_flux = spectral_flux.mean()
|
||||
std_flux = spectral_flux.std()
|
||||
if std_flux > 0:
|
||||
kurtosis = torch.mean(((spectral_flux - mean_flux) / std_flux) ** 4)
|
||||
beat_clarity = safe_float(torch.clamp(kurtosis / 10.0, 0, 1))
|
||||
else:
|
||||
beat_clarity = 0.3
|
||||
else:
|
||||
beat_clarity = 0.5
|
||||
|
||||
# 节奏稳定性 - onset 间隔的变异系数
|
||||
if len(spectral_flux) > 20:
|
||||
# 简单检测峰值
|
||||
peaks = (spectral_flux[1:-1] > spectral_flux[:-2]) & (spectral_flux[1:-1] > spectral_flux[2:])
|
||||
peak_indices = torch.where(peaks)[0]
|
||||
if len(peak_indices) > 2:
|
||||
intervals = torch.diff(peak_indices.float())
|
||||
cv = intervals.std() / intervals.mean() if intervals.mean() > 0 else 1.0
|
||||
stability = safe_float(1.0 - torch.clamp(cv, 0, 1))
|
||||
else:
|
||||
stability = 0.5
|
||||
else:
|
||||
stability = 0.5
|
||||
|
||||
# 律动感 - 基于 onset strength 的方差（高方差 = 更多动态变化 = 更有律动）
|
||||
if len(onset_env) > 0:
|
||||
onset_std = np.std(onset_env)
|
||||
onset_mean = np.mean(onset_env)
|
||||
# 律动感 - onset 的方差/均值
|
||||
if len(spectral_flux) > 0:
|
||||
onset_mean = spectral_flux.mean()
|
||||
onset_std = spectral_flux.std()
|
||||
if onset_mean > 0:
|
||||
groove = safe_float(min(onset_std / onset_mean, 1.0))
|
||||
groove = safe_float(torch.clamp(onset_std / onset_mean, 0, 1))
|
||||
else:
|
||||
groove = 0.0
|
||||
else:
|
||||
groove = 0.0
|
||||
|
||||
# 子维度评分（均为 0-10）
|
||||
scores = {
|
||||
"bpm": bpm,
|
||||
"bpm": round(safe_float(bpm), 1),
|
||||
"beat_clarity": round(beat_clarity * 10, 2),
|
||||
"stability": round(stability * 10, 2),
|
||||
"groove": round(groove * 10, 2),
|
||||
}
|
||||
|
||||
# 维度综合分（不限制 BPM，只评估质量）
|
||||
score = (
|
||||
0.35 * scores["beat_clarity"] +
|
||||
0.30 * scores["stability"] +
|
||||
|
||||
@ -1,42 +1,46 @@
|
||||
"""音色分析 - 音色丰富度、频谱平衡"""
|
||||
import numpy as np
|
||||
"""音色分析 - GPU 版本"""
|
||||
import torch
|
||||
import torchaudio
|
||||
from . import safe_float
|
||||
|
||||
|
||||
def analyze_timbre(y, sr):
|
||||
"""
|
||||
分析音色维度
|
||||
"""
|
||||
import librosa
|
||||
def analyze_timbre(y, sr, stft_result=None):
|
||||
"""分析音色维度 (GPU)"""
|
||||
device = y.device
|
||||
|
||||
# 频谱质心 (brightness)
|
||||
spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
|
||||
centroid_mean = safe_float(np.mean(spectral_centroid))
|
||||
if stft_result is None:
|
||||
stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
|
||||
magnitude = stft_result.abs()
|
||||
|
||||
# MFCC (13维音色指纹)
|
||||
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
|
||||
mfcc_mean = np.mean(mfcc, axis=1)
|
||||
freqs = torch.fft.fftfreq(2048, 1.0/sr)[:1025]
|
||||
|
||||
# 音色丰富度 - MFCC 的方差（方差大 = 音色变化丰富）
|
||||
mfcc_variance = np.var(mfcc, axis=1)
|
||||
richness = safe_float(np.mean(mfcc_variance))
|
||||
richness_norm = min(richness / 100, 1.0) # 归一化
|
||||
# 频谱质心
|
||||
freq_sum = (freqs.unsqueeze(1) * magnitude).sum(dim=0)
|
||||
mag_sum = magnitude.sum(dim=0)
|
||||
spectral_centroid = freq_sum / torch.clamp(mag_sum, 1e-10)
|
||||
centroid_mean = safe_float(spectral_centroid.mean())
|
||||
|
||||
# 频谱平衡 - 各频段能量分布的均匀度
|
||||
S = np.abs(librosa.stft(y))
|
||||
freqs = librosa.fft_frequencies(sr=sr)
|
||||
# MFCC (简化版 - 用 mel 滤波器组)
|
||||
mel_spec = torchaudio.transforms.MelSpectrogram(
|
||||
sample_rate=sr, n_fft=2048, hop_length=512, n_mels=40
|
||||
).to(device)(y)
|
||||
mel_log = torch.log(torch.clamp(mel_spec, 1e-10))
|
||||
|
||||
# 分频段：低频 (<250Hz), 中频 (250-2000Hz), 高频 (>2000Hz)
|
||||
low = np.sum(S[freqs < 250] ** 2)
|
||||
mid = np.sum((freqs >= 250) & (freqs < 2000))
|
||||
mid_energy = np.sum(S[(freqs >= 250) & (freqs < 2000)] ** 2)
|
||||
high_energy = np.sum(S[freqs >= 2000] ** 2)
|
||||
# DCT 得到 MFCC
|
||||
mfcc = torch.fft.fft(mel_log, dim=0).real[:13]
|
||||
mfcc_variance = torch.var(mfcc, dim=1)
|
||||
richness = safe_float(mfcc_variance.mean())
|
||||
richness_norm = min(richness / 100, 1.0)
|
||||
|
||||
# 频谱平衡
|
||||
power = magnitude ** 2
|
||||
low = power[freqs < 250].sum()
|
||||
mid = power[(freqs >= 250) & (freqs < 2000)].sum()
|
||||
high = power[freqs >= 2000].sum()
|
||||
total = low + mid + high
|
||||
|
||||
total = low + mid_energy + high_energy
|
||||
if total > 0:
|
||||
ratios = [low / total, mid_energy / total, high_energy / total]
|
||||
# 理想比例：低频 0.2-0.4，中频 0.3-0.5，高频 0.1-0.3
|
||||
# 计算与理想比例的偏差
|
||||
ratios = [safe_float(low / total), safe_float(mid / total), safe_float(high / total)]
|
||||
ideal = [0.3, 0.4, 0.2]
|
||||
deviation = sum(abs(r - i) for r, i in zip(ratios, ideal))
|
||||
balance = safe_float(1.0 - min(deviation / 1.0, 1.0))
|
||||
|
||||
@ -1,45 +1,53 @@
|
||||
"""调性分析 - 调性清晰度、和声丰富度、转调合理性"""
|
||||
import numpy as np
|
||||
"""调性分析 - GPU 版本"""
|
||||
import torch
|
||||
from . import safe_float
|
||||
|
||||
|
||||
def analyze_tonality(y, sr):
|
||||
"""
|
||||
分析调性维度
|
||||
"""
|
||||
import librosa
|
||||
def analyze_tonality(y, sr, stft_result=None):
|
||||
"""分析调性维度 (GPU)"""
|
||||
device = y.device
|
||||
|
||||
# Chroma 特征
|
||||
chroma = librosa.feature.chroma_stft(y=y, sr=sr)
|
||||
if stft_result is None:
|
||||
stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
|
||||
magnitude = stft_result.abs()
|
||||
|
||||
# 调性清晰度 - chroma 的峰值与均值之比
|
||||
chroma_mean = np.mean(chroma, axis=1)
|
||||
if np.sum(chroma_mean) > 0:
|
||||
peak_ratio = np.max(chroma_mean) / np.mean(chroma_mean)
|
||||
key_clarity = safe_float(min((peak_ratio - 1.0) / 2.0, 1.0)) # 峰值是均值的 2 倍以上满分
|
||||
else:
|
||||
key_clarity = 0.0
|
||||
freqs = torch.fft.fftfreq(2048, 1.0/sr)[:1025]
|
||||
|
||||
# 和声丰富度 - 活跃音级数量
|
||||
active_notes = np.sum(chroma_mean > np.mean(chroma_mean) * 0.5)
|
||||
harmony = safe_float(min(active_notes / 7.0, 1.0)) # 7 个以上活跃音级满分
|
||||
# Chroma 特征 (12 音级)
|
||||
chroma = torch.zeros(12, device=device)
|
||||
note_freqs = 440 * (2 ** (torch.arange(12, device=device, dtype=torch.float32) / 12))
|
||||
for i, nf in enumerate(note_freqs):
|
||||
bin_idx = torch.argmin(torch.abs(freqs - nf)).item()
|
||||
if bin_idx < magnitude.shape[0]:
|
||||
chroma[i] = magnitude[bin_idx].mean()
|
||||
|
||||
# 转调检测 - 滑动窗口 chroma 变化
|
||||
hop = chroma.shape[1] // 4
|
||||
if hop > 10 and chroma.shape[1] > hop * 2:
|
||||
seg1 = np.mean(chroma[:, :hop], axis=1)
|
||||
seg4 = np.mean(chroma[:, -hop:], axis=1)
|
||||
# 余弦相似度
|
||||
cos_sim = np.dot(seg1, seg4) / (np.linalg.norm(seg1) * np.linalg.norm(seg4) + 1e-10)
|
||||
# 相似度高 = 没转调（稳定），中等 = 有转调（合理），低 = 大转调
|
||||
chroma = chroma / torch.clamp(chroma.sum(), 1e-10)
|
||||
|
||||
# 调性清晰度
|
||||
chroma_mean = chroma
|
||||
peak_ratio = chroma_mean.max() / torch.clamp(chroma_mean.mean(), 1e-10)
|
||||
key_clarity = safe_float(torch.clamp((peak_ratio - 1.0) / 2.0, 0, 1))
|
||||
|
||||
# 和声丰富度
|
||||
active_notes = (chroma_mean > chroma_mean.mean() * 0.5).sum().item()
|
||||
harmony = safe_float(min(active_notes / 7.0, 1.0))
|
||||
|
||||
# 转调检测 (简化 - 前后段 chroma 差异)
|
||||
n_frames = magnitude.shape[1]
|
||||
hop = n_frames // 4
|
||||
if hop > 10 and n_frames > hop * 2:
|
||||
seg1 = magnitude[:, :hop].mean(dim=1)
|
||||
seg4 = magnitude[:, -hop:].mean(dim=1)
|
||||
cos_sim = torch.dot(seg1, seg4) / (torch.norm(seg1) * torch.norm(seg4) + 1e-10)
|
||||
cos_sim = safe_float(cos_sim)
|
||||
if cos_sim > 0.8:
|
||||
modulation_score = 0.8 # 稳定
|
||||
modulation_score = 0.8
|
||||
elif cos_sim > 0.5:
|
||||
modulation_score = 1.0 # 适度转调
|
||||
modulation_score = 1.0
|
||||
else:
|
||||
modulation_score = 0.5 # 大转调
|
||||
modulation_score = 0.5
|
||||
else:
|
||||
modulation_score = 0.7 # 无法判断，给中等分
|
||||
modulation_score = 0.7
|
||||
|
||||
scores = {
|
||||
"key_clarity": round(key_clarity * 10, 2),
|
||||
@ -47,11 +55,7 @@ def analyze_tonality(y, sr):
|
||||
"modulation": round(modulation_score * 10, 2),
|
||||
}
|
||||
|
||||
score = (
|
||||
0.40 * scores["key_clarity"] +
|
||||
0.35 * scores["harmony"] +
|
||||
0.25 * scores["modulation"]
|
||||
)
|
||||
score = 0.40 * scores["key_clarity"] + 0.35 * scores["harmony"] + 0.25 * scores["modulation"]
|
||||
scores["score"] = round(min(score, 10), 2)
|
||||
|
||||
return scores
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
"""主评估逻辑 - 编排所有分析器，返回加权总分"""
|
||||
"""主评估逻辑 - GPU 版本，预计算公共特征"""
|
||||
import os
|
||||
import numpy as np
|
||||
import torch
|
||||
from .scenes import get_scene_config, SCENES, DIMENSIONS
|
||||
from .analyzers import load_audio, get_device
|
||||
from .analyzers.rhythm import analyze_rhythm
|
||||
from .analyzers.danceability import analyze_danceability
|
||||
from .analyzers.energy import analyze_energy
|
||||
@ -24,7 +25,7 @@ ANALYZER_MAP = {
|
||||
|
||||
def evaluate_song(filepath, scene="pop"):
|
||||
"""
|
||||
评估一首歌曲
|
||||
评估一首歌曲 (GPU 加速)
|
||||
|
||||
参数:
|
||||
filepath: 音频文件路径
|
||||
@ -34,29 +35,33 @@ def evaluate_song(filepath, scene="pop"):
|
||||
dict: {
|
||||
"total_score": float,
|
||||
"scene": str,
|
||||
"dimensions": [
|
||||
{"key": str, "name": str, "score": float, "weight": float, "weighted": float, "details": dict}
|
||||
]
|
||||
"device": str,
|
||||
"dimensions": [...]
|
||||
}
|
||||
"""
|
||||
import librosa
|
||||
|
||||
if scene not in SCENES:
|
||||
return {"error": f"未知场景: {scene}", "available_scenes": list(SCENES.keys())}
|
||||
|
||||
if not os.path.exists(filepath):
|
||||
return {"error": f"文件不存在: {filepath}"}
|
||||
|
||||
# 加载音频
|
||||
device = get_device()
|
||||
|
||||
# 加载音频到 GPU
|
||||
try:
|
||||
y, sr = librosa.load(filepath, sr=22050)
|
||||
y, sr = load_audio(filepath, sr=22050)
|
||||
except Exception as e:
|
||||
return {"error": f"音频加载失败: {str(e)}"}
|
||||
|
||||
# 预计算 STFT (所有分析器共享)
|
||||
try:
|
||||
stft_result = torch.stft(y, n_fft=2048, hop_length=512, return_complex=True)
|
||||
except Exception as e:
|
||||
return {"error": f"STFT 计算失败: {str(e)}"}
|
||||
|
||||
config = get_scene_config(scene)
|
||||
weights = config["weights"]
|
||||
|
||||
# 运行各维度分析器（仅运行启用且权重 > 0 的维度）
|
||||
dimensions = []
|
||||
total_weighted = 0.0
|
||||
|
||||
@ -69,7 +74,7 @@ def evaluate_song(filepath, scene="pop"):
|
||||
continue
|
||||
|
||||
try:
|
||||
result = analyzer(y, sr)
|
||||
result = analyzer(y, sr, stft_result=stft_result)
|
||||
dim_score = result.pop("score", 0)
|
||||
dim_name = DIMENSIONS.get(dim_key, {}).get("name", dim_key)
|
||||
weighted = dim_score * weight
|
||||
@ -93,12 +98,17 @@ def evaluate_song(filepath, scene="pop"):
|
||||
"details": {"error": str(e)}
|
||||
})
|
||||
|
||||
# 按权重排序
|
||||
# 清理 GPU 内存
|
||||
del y, stft_result
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
dimensions.sort(key=lambda d: d["weight"], reverse=True)
|
||||
|
||||
return {
|
||||
"total_score": round(total_weighted, 2),
|
||||
"scene": scene,
|
||||
"scene_name": config["name"],
|
||||
"device": str(device),
|
||||
"dimensions": dimensions
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user