yumoqing
b9728f9bf8
- 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
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
"""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, 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
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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),
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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_sorted, _ = torch.sort(rms)
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n = len(rms_sorted)
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noise_floor = rms_sorted[:n // 10].mean()
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signal_level = rms_sorted[-n // 10:].mean()
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if noise_floor > 0:
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snr_db = safe_float(20 * torch.log10(signal_level / noise_floor))
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else:
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snr_db = 60.0
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else:
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snr_db = 30.0
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# 削波检测
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clipped = (torch.abs(y) > 0.99).sum().item() / len(y)
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clipping_score = 10.0 if clipped < 0.001 else max(0, 10.0 - clipped * 10000)
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# 频率均衡 - 频谱平坦度
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if stft_result is not None:
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magnitude = stft_result.abs()
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geometric_mean = torch.exp(torch.log(torch.clamp(magnitude, 1e-10)).mean(dim=0))
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arithmetic_mean = magnitude.mean(dim=0)
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flatness = geometric_mean / torch.clamp(arithmetic_mean, 1e-10)
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flatness_mean = safe_float(flatness.mean())
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else:
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flatness_mean = 0.05
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if 0.001 <= flatness_mean <= 0.1:
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freq_balance = 8.0
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elif flatness_mean < 0.001:
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freq_balance = 6.0
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else:
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freq_balance = 5.0
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scores = {
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"snr": round(snr_db, 2),
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"clipping": round(clipping_score, 2),
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"frequency_balance": round(freq_balance, 2),
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}
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if snr_db >= 40:
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snr_score = 10.0
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elif snr_db >= 20:
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snr_score = 6.0 + (snr_db - 20) / 20 * 4.0
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else:
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snr_score = max(snr_db / 20 * 6.0, 0)
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score = 0.40 * snr_score + 0.35 * scores["clipping"] + 0.25 * scores["frequency_balance"]
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scores["score"] = round(min(score, 10), 2)
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return scores
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