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