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from typing import Optional
from typing import Tuple
from typing import Union
import torch
from .nets_utils import make_pad_mask
class Stft(torch.nn.Module):
def __init__(
self,
n_fft: int = 512,
win_length: Union[int, None] = 512,
hop_length: int = 128,
center: bool = True,
pad_mode: str = "reflect",
normalized: bool = False,
onesided: bool = True,
kaldi_padding_mode=False,
):
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.pad_mode = pad_mode
self.normalized = normalized
self.onesided = onesided
self.kaldi_padding_mode = kaldi_padding_mode
if self.kaldi_padding_mode:
self.win_length = 400
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"pad_mode={self.pad_mode}, "
f"normalized={self.normalized}, "
f"onesided={self.onesided}"
)
def forward(
self, input: torch.Tensor, ilens: torch.Tensor = None
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""STFT forward function.
Args:
input: (Batch, Nsamples) or (Batch, Nsample, Channels)
ilens: (Batch)
Returns:
output: (Batch, Frames, Freq, 2) or (Batch, Frames, Channels, Freq, 2)
"""
bs = input.size(0)
if input.dim() == 3:
multi_channel = True
# input: (Batch, Nsample, Channels) -> (Batch * Channels, Nsample)
input = input.transpose(1, 2).reshape(-1, input.size(1))
else:
multi_channel = False
# output: (Batch, Freq, Frames, 2=real_imag)
# or (Batch, Channel, Freq, Frames, 2=real_imag)
if not self.kaldi_padding_mode:
output = torch.stft(
input,
n_fft=self.n_fft,
win_length=self.win_length,
hop_length=self.hop_length,
center=self.center,
pad_mode=self.pad_mode,
normalized=self.normalized,
onesided=self.onesided,
return_complex=False
)
else:
# NOTE(sx): Use Kaldi-fasion padding, maybe wrong
num_pads = self.n_fft - self.win_length
input = torch.nn.functional.pad(input, (num_pads, 0))
output = torch.stft(
input,
n_fft=self.n_fft,
win_length=self.win_length,
hop_length=self.hop_length,
center=False,
pad_mode=self.pad_mode,
normalized=self.normalized,
onesided=self.onesided,
return_complex=False
)
# 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 = torch.div(ilens - self.win_length, self.hop_length, rounding_mode='floor') + 1
# 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
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