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"""
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
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