Delete src/miocodec/pupu/modules/anti_aliasing/resample.py with huggingface_hub

src/miocodec/pupu/modules/anti_aliasing/resample.py

@@ -1,55 +0,0 @@
-import torch.nn as nn
-from torch.nn import functional as F
-
-from .filter import *
-
-
-class UpSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = (
-            int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        )
-        self.stride = ratio
-        self.pad = self.kernel_size // ratio - 1
-        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
-        self.pad_right = (
-            self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
-        )
-        filter = kaiser_sinc_filter1d(
-            cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size
-        )
-        self.register_buffer("filter", filter)
-
-    # x: [B, C, T]
-    def forward(self, x):
-        _, C, _ = x.shape
-
-        x = F.pad(x, (self.pad, self.pad), mode="replicate")
-        x = self.ratio * F.conv_transpose1d(
-            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C
-        )
-        x = x[..., self.pad_left : -self.pad_right]
-
-        return x
-
-
-class DownSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = (
-            int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        )
-        self.lowpass = LowPassFilter1d(
-            cutoff=0.5 / ratio,
-            half_width=0.6 / ratio,
-            stride=ratio,
-            kernel_size=self.kernel_size,
-        )
-
-    def forward(self, x):
-        xx = self.lowpass(x)
-
-        return xx