convt stft

enhancer/utils/transforms.py

@@ -0,0 +1,63 @@
+from typing import Optional
+
+import numpy as np
+import torch
+from scipy.signal import get_window
+from torch import nn
+
+
+class ConvFFT(nn.Module):
+    def __init__(
+        self,
+        window_len: int,
+        nfft: Optional[int] = None,
+        window: str = "hamming",
+    ):
+        self.window_len = window_len
+        self.nfft = nfft if nfft else np.int(2 ** np.ceil(np.log2(window_len)))
+        self.window = get_window(window, window_len)
+
+    @property
+    def init_kernel(self):
+
+        fourier_basis = np.fft.rfft(np.eye(self.nfft))[: self.window_len]
+        real, imag = np.real(fourier_basis), np.imag(fourier_basis)
+        kernel = np.concatenate([real, imag], 1).T
+        kernel *= self.window
+        return torch.from_numpy(kernel)
+
+
+class ConvSTFT(ConvFFT):
+    def __init__(
+        self,
+        window_len: int,
+        hop_size: Optional[int] = None,
+        nfft: Optional[int] = None,
+        window: str = "hamming",
+    ):
+        super(self, ConvSTFT).__init__(
+            window_len=window_len, nfft=nfft, window=window
+        )
+        self.hop_size = hop_size if hop_size else window_len // 2
+        self.register_buffer("weight", self.init_kernel)
+
+    def forward(self, input):
+        pass
+
+
+class ConviSTFT(ConvFFT):
+    def __init__(
+        self,
+        window_len: int,
+        hop_size: Optional[int] = None,
+        nfft: Optional[int] = None,
+        window: str = "hamming",
+    ):
+        super(self, ConvSTFT).__init__(
+            window_len=window_len, nfft=nfft, window=window
+        )
+        self.hop_size = hop_size if hop_size else window_len // 2
+        self.register_buffer("weight", self.init_kernel)
+
+    def forward(self, input):
+        pass