feat(assets): SA3 音效补齐 radio 11+1 与 dungeon 13源+15pan（token 走 hf-mirror 实测可用）

- 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

tools/stable-audio-3/stable_audio_3/models/bottleneck.py

@@ -0,0 +1,71 @@
+import torch
+from torch import nn
+
+class SoftNormBottleneck(nn.Module):
+    def __init__(self, dim = 32, noise_augment_dim=0, noise_regularize = False, auto_scale = False, freeze = False):
+        super().__init__()
+
+        self.noise_augment_dim = noise_augment_dim
+        self.scaling_factor = nn.Parameter(torch.ones(1,dim,1))
+        self.bias = nn.Parameter(torch.zeros(1,dim,1))
+        self.noise_scaling_factor = nn.Parameter(torch.ones(1,noise_augment_dim,1))
+        self.noise_regularize = noise_regularize
+        self.freeze = freeze
+        if self.freeze:
+            self.scaling_factor.requires_grad = False
+            self.bias.requires_grad = False
+            self.noise_scaling_factor.requires_grad = False
+        if auto_scale:
+            running_std = torch.ones(1)
+            self.register_parameter("running_std", nn.Parameter(running_std, requires_grad=False))
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        x = x * self.scaling_factor + self.bias
+
+        if self.training and hasattr(self,"running_std") and not self.freeze:
+            # Update running std
+            self.running_std.data = (self.running_std.data * 0.999 + x.std().detach() * 0.001).clamp(min = 1e-4)
+
+        if hasattr(self, "running_std"):
+            x = x / self.running_std
+
+        if self.training and return_info:
+            var = (x.std(dim=-1) ** 2).clip(min = 1e-4)
+            logvar = torch.log(var)
+            mean = x.mean(dim=-1)
+            loss = (mean * mean + var - logvar - 1).mean()
+            var = (x.std(dim=-2) ** 2).clip(min = 1e-4)
+            logvar = torch.log(var)
+            mean = x.mean(dim=-2)
+            loss = loss + 0.4 * (mean * mean + var - logvar - 1).mean()
+            info["softnorm_loss"] = loss
+
+        if return_info:
+            return x, info
+
+        return x
+
+    def decode(self, x, **kwargs):
+        if hasattr(self, "running_std"):
+            x = x * self.running_std
+
+        if self.noise_regularize:
+            if hasattr(self, "running_std"):
+                scaling = self.running_std
+            else:
+                scaling = x.std(dim = -1).unsqueeze(-1)
+            if self.training:
+                scale = 5e-2
+            else:
+                scale = 1e-3
+            noise = torch.randn_like(x) * scaling * scale
+            x = x + noise
+
+        if self.noise_augment_dim > 0:
+            noise = self.noise_scaling_factor * torch.randn(x.shape[0], self.noise_augment_dim,
+                                x.shape[-1]).type_as(x)
+            x = torch.cat([x, noise], dim=1)
+
+        return x