Upload STFT.py

STFT.py

@@ -0,0 +1,118 @@
+"""
+Taken from ESPNet
+"""
+
+import torch
+from torch.functional import stft as torch_stft
+from torch_complex.tensor import ComplexTensor
+
+from Utility.utils import make_pad_mask
+
+
+class STFT(torch.nn.Module):
+
+    def __init__(self, n_fft=512, win_length=None, hop_length=128, window="hann", center=True, normalized=False,
+                 onesided=True):
+        super().__init__()
+        self.n_fft = n_fft
+        if win_length is None:
+            self.win_length = n_fft
+        else:
+            self.win_length = win_length
+        self.hop_length = hop_length
+        self.center = center
+        self.normalized = normalized
+        self.onesided = onesided
+        self.window = window
+
+    def extra_repr(self):
+        return (f"n_fft={self.n_fft}, "
+                f"win_length={self.win_length}, "
+                f"hop_length={self.hop_length}, "
+                f"center={self.center}, "
+                f"normalized={self.normalized}, "
+                f"onesided={self.onesided}")
+
+    def forward(self, input_wave, ilens=None):
+        """
+        STFT forward function.
+        Args:
+            input_wave: (Batch, Nsamples) or (Batch, Nsample, Channels)
+            ilens: (Batch)
+        Returns:
+            output: (Batch, Frames, Freq, 2) or (Batch, Frames, Channels, Freq, 2)
+        """
+        bs = input_wave.size(0)
+
+        if input_wave.dim() == 3:
+            multi_channel = True
+            # input: (Batch, Nsample, Channels) -> (Batch * Channels, Nsample)
+            input_wave = input_wave.transpose(1, 2).reshape(-1, input_wave.size(1))
+        else:
+            multi_channel = False
+
+        # output: (Batch, Freq, Frames, 2=real_imag)
+        # or (Batch, Channel, Freq, Frames, 2=real_imag)
+        if self.window is not None:
+            window_func = getattr(torch, f"{self.window}_window")
+            window = window_func(self.win_length, dtype=input_wave.dtype, device=input_wave.device)
+        else:
+            window = None
+
+        complex_output = torch_stft(input=input_wave,
+                                    n_fft=self.n_fft,
+                                    win_length=self.win_length,
+                                    hop_length=self.hop_length,
+                                    center=self.center,
+                                    window=window,
+                                    normalized=self.normalized,
+                                    onesided=self.onesided,
+                                    return_complex=True)
+        output = torch.view_as_real(complex_output)
+        # output: (Batch, Freq, Frames, 2=real_imag)
+        # -> (Batch, Frames, Freq, 2=real_imag)
+        output = output.transpose(1, 2)
+        if multi_channel:
+            # output: (Batch * Channel, Frames, Freq, 2=real_imag)
+            # -> (Batch, Frame, Channel, Freq, 2=real_imag)
+            output = output.view(bs, -1, output.size(1), output.size(2), 2).transpose(1, 2)
+
+        if ilens is not None:
+            if self.center:
+                pad = self.win_length // 2
+                ilens = ilens + 2 * pad
+
+            olens = (ilens - self.win_length) // self.hop_length + 1
+            output.masked_fill_(make_pad_mask(olens, output, 1), 0.0)
+        else:
+            olens = None
+
+        return output, olens
+
+    def inverse(self, input, ilens=None):
+        """
+        Inverse STFT.
+        Args:
+            input: Tensor(batch, T, F, 2) or ComplexTensor(batch, T, F)
+            ilens: (batch,)
+        Returns:
+            wavs: (batch, samples)
+            ilens: (batch,)
+        """
+        istft = torch.functional.istft
+
+        if self.window is not None:
+            window_func = getattr(torch, f"{self.window}_window")
+            window = window_func(self.win_length, dtype=input.dtype, device=input.device)
+        else:
+            window = None
+
+        if isinstance(input, ComplexTensor):
+            input = torch.stack([input.real, input.imag], dim=-1)
+        assert input.shape[-1] == 2
+        input = input.transpose(1, 2)
+
+        wavs = istft(input, n_fft=self.n_fft, hop_length=self.hop_length, win_length=self.win_length, window=window, center=self.center,
+                     normalized=self.normalized, onesided=self.onesided, length=ilens.max() if ilens is not None else ilens)
+
+        return wavs, ilens