xsl
24c526af68
- 模型 17/17 文件经镜像全量缓存（gated+token，直连本机不通；sfx.md/setup.md/asset-pipeline.md 镜像结论同步更新） - 修复迁移遗留：stable_audio_3/models/ 源码子包被 .gitignore models/ 规则误伤丢失，从上游恢复并加白名单 - radio：arg_muffled/brake_screech 等叙事件落地（接线原有 L111/L154 零改动）+ phone_ring SYN→SA3 升级；dungeon 13 源 A/B 对齐（RMS 偏差 ≤0.3dB）全量同名替换，SYN 原版备份 tools/asset_fix_backup/sa3_ab_20260902/ - 关键玩法语义达标：echo 距离档 0.83<1.43<2.18<2.98s 严格递增；one-shot 真峰值全 ≤-3dBFS（解码回读法） - check_all_assets 8 游戏全绿（radio 254/254、dungeon 303/303）；playtest radio 221/dungeon 1610 全绿
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
import torch
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from torch import nn
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class SoftNormBottleneck(nn.Module):
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def __init__(self, dim = 32, noise_augment_dim=0, noise_regularize = False, auto_scale = False, freeze = False):
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super().__init__()
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self.noise_augment_dim = noise_augment_dim
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self.scaling_factor = nn.Parameter(torch.ones(1,dim,1))
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self.bias = nn.Parameter(torch.zeros(1,dim,1))
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self.noise_scaling_factor = nn.Parameter(torch.ones(1,noise_augment_dim,1))
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self.noise_regularize = noise_regularize
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self.freeze = freeze
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if self.freeze:
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self.scaling_factor.requires_grad = False
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self.bias.requires_grad = False
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self.noise_scaling_factor.requires_grad = False
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if auto_scale:
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running_std = torch.ones(1)
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self.register_parameter("running_std", nn.Parameter(running_std, requires_grad=False))
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def encode(self, x, return_info=False, **kwargs):
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info = {}
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x = x * self.scaling_factor + self.bias
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if self.training and hasattr(self,"running_std") and not self.freeze:
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# Update running std
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self.running_std.data = (self.running_std.data * 0.999 + x.std().detach() * 0.001).clamp(min = 1e-4)
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if hasattr(self, "running_std"):
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x = x / self.running_std
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if self.training and return_info:
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var = (x.std(dim=-1) ** 2).clip(min = 1e-4)
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logvar = torch.log(var)
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mean = x.mean(dim=-1)
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loss = (mean * mean + var - logvar - 1).mean()
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var = (x.std(dim=-2) ** 2).clip(min = 1e-4)
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logvar = torch.log(var)
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mean = x.mean(dim=-2)
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loss = loss + 0.4 * (mean * mean + var - logvar - 1).mean()
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info["softnorm_loss"] = loss
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if return_info:
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return x, info
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return x
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def decode(self, x, **kwargs):
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if hasattr(self, "running_std"):
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x = x * self.running_std
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if self.noise_regularize:
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if hasattr(self, "running_std"):
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scaling = self.running_std
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else:
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scaling = x.std(dim = -1).unsqueeze(-1)
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if self.training:
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scale = 5e-2
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else:
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scale = 1e-3
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noise = torch.randn_like(x) * scaling * scale
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x = x + noise
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if self.noise_augment_dim > 0:
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noise = self.noise_scaling_factor * torch.randn(x.shape[0], self.noise_augment_dim,
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x.shape[-1]).type_as(x)
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x = torch.cat([x, noise], dim=1)
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return x
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