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/blocks.py

@@ -0,0 +1,84 @@
+import math
+import torch
+from torch import nn
+from torch.nn.utils import weight_norm
+
+
+def get_activation(activation, channels=None) -> nn.Module:
+    if activation == "elu":
+        return nn.ELU()
+    elif activation == "none":
+        return nn.Identity()
+    else:
+        raise ValueError(f"Unknown activation {activation}")
+
+def WNConv1d(*args, **kwargs):
+    return weight_norm(nn.Conv1d(*args, **kwargs))
+
+class ResidualUnit(nn.Module):
+    def __init__(self, in_channels, out_channels, dilation, depthwise=False, bias=True):
+        super().__init__()
+
+        self.dilation = dilation
+
+        padding = (dilation * (7-1)) // 2
+
+        self.layers = nn.Sequential(
+            get_activation("elu"),
+            WNConv1d(in_channels=in_channels, out_channels=out_channels,
+                      kernel_size=7, dilation=dilation, padding=padding, groups=1 if not depthwise else out_channels, bias=bias),
+            get_activation("elu"),
+            WNConv1d(in_channels=out_channels, out_channels=out_channels,
+                      kernel_size=1, bias=bias)
+        )
+
+    def forward(self, x):
+        res = x
+        x = self.layers(x)
+        return x + res
+
+class FourierFeatures(nn.Module):
+    def __init__(self, in_features, out_features, std=16.):
+        super().__init__()
+        assert out_features % 2 == 0
+        self.register_buffer('weight', torch.randn([out_features // 2, in_features]) * std)
+
+    def forward(self, input):
+        f = 2 * math.pi * input @ self.weight.T
+        return torch.cat([f.cos(), f.sin()], dim=-1)
+
+class ExpoFourierFeatures(nn.Module):
+    def __init__(self, dim, min_freq=0.5, max_freq=10000.0):
+        super().__init__()
+        self.dim = dim
+        self.min_freq = min_freq
+        self.max_freq = max_freq
+
+    @torch.amp.autocast("cuda",enabled=False)
+    def forward(self, t):
+        """
+        t: [B] tensor.
+        """
+        in_dtype = t.dtype
+        t = t.float()
+
+        if t.dim() == 1:
+            t = t.unsqueeze(-1)
+
+        half_dim = self.dim // 2
+
+        # Calculate frequencies (safely in FP32)
+        ramp = torch.linspace(0, 1, half_dim, device=t.device, dtype=torch.float32)
+        log_min = math.log(self.min_freq)
+        log_max = math.log(self.max_freq)
+
+        freqs = torch.exp(ramp * (log_max - log_min) + log_min)
+
+        # Calculate arguments (safely in FP32)
+        args = t * freqs * 2 * math.pi
+
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+
+        return embedding.to(in_dtype)
+
+