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
3.3 KiB
Python
85 lines
3.3 KiB
Python
import torch
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import torch.nn as nn
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from einops import rearrange
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from torchaudio.transforms import Resample
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from .blocks import ResidualUnit, WNConv1d
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class AutoencoderPretransform(nn.Module):
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def __init__(self, model, scale=1.0, iterate_batch=False, chunked=False):
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super().__init__()
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self.model = model
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self.model.requires_grad_(False).eval()
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self.scale = scale
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self.downsampling_ratio = model.downsampling_ratio
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self.io_channels = model.io_channels
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self.enable_grad = False
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self.iterate_batch = iterate_batch
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self.chunked = chunked
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def encode(self, x, **kwargs):
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return self.model.encode_audio(x, chunked=self.chunked, iterate_batch=self.iterate_batch, **kwargs) / self.scale
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def decode(self, z, chunked=None, **kwargs):
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chunked = self.chunked if chunked is None else chunked
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return self.model.decode_audio(z * self.scale, chunked=chunked, iterate_batch=self.iterate_batch, **kwargs)
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def fold_channels_into_batch(x):
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x = rearrange(x, 'b c ... -> (b c) ...')
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return x
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def unfold_channels_from_batch(x, channels):
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if channels == 1:
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return x.unsqueeze(1)
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x = rearrange(x, '(b c) ... -> b c ...', c = channels)
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return x
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class PatchedPretransform(nn.Module):
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def __init__(self, channels, patch_size, oversampling = 1, postfilter_channels = 0, **kwargs):
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super().__init__()
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self.channels = channels
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self.patch_size = patch_size
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self.oversampling = oversampling
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self.downsampling_ratio = patch_size
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self.io_channels = channels
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self.encoded_channels = channels * patch_size
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self.enable_grad = False
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if self.oversampling > 1:
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self.input_upsampler = Resample(1, self.oversampling)
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self.output_downsampler = Resample(self.oversampling, 1)
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if postfilter_channels > 0:
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self.postfilter = nn.Sequential(
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WNConv1d(in_channels=channels, out_channels=postfilter_channels, kernel_size=7, padding=3, bias=True),
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ResidualUnit(in_channels=postfilter_channels, out_channels=postfilter_channels,
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dilation=1, use_snake=True, bias = True),
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ResidualUnit(in_channels=postfilter_channels, out_channels=postfilter_channels,
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dilation=3, use_snake=True, bias = True),
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ResidualUnit(in_channels=postfilter_channels, out_channels=postfilter_channels,
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dilation=9, use_snake=True, bias = True),
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WNConv1d(in_channels=postfilter_channels, out_channels=channels, kernel_size=7, padding=3, bias=False))
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def _pad(self, x):
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seq_len = x.shape[-1]
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pad_len = (self.patch_size - (seq_len % self.patch_size)) % self.patch_size
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if pad_len > 0:
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x = torch.cat([x, torch.zeros_like(x[:, :, :pad_len])], dim=-1)
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return x
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def encode(self, x):
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if self.oversampling > 1:
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x = self.input_upsampler(x)
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x = self._pad(x)
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x = rearrange(x, "b c (l h) -> b (c h) l", h=self.patch_size)
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return x
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def decode(self, x):
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x = rearrange(x, "b (c h) l -> b c (l h)", h=self.patch_size)
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if hasattr(self, 'postfilter'):
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x = self.postfilter(x)
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if self.oversampling > 1:
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x = self.output_downsampler(x)
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return x
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