119 lines
3.8 KiB
Python
119 lines
3.8 KiB
Python
from typing import Optional
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from typing import Tuple
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from typing import Union
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import torch
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from .nets_utils import make_pad_mask
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class Stft(torch.nn.Module):
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def __init__(
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self,
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n_fft: int = 512,
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win_length: Union[int, None] = 512,
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hop_length: int = 128,
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center: bool = True,
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pad_mode: str = "reflect",
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normalized: bool = False,
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onesided: bool = True,
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kaldi_padding_mode=False,
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):
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super().__init__()
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self.n_fft = n_fft
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if win_length is None:
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self.win_length = n_fft
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else:
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self.win_length = win_length
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self.hop_length = hop_length
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self.center = center
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self.pad_mode = pad_mode
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self.normalized = normalized
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self.onesided = onesided
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self.kaldi_padding_mode = kaldi_padding_mode
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if self.kaldi_padding_mode:
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self.win_length = 400
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def extra_repr(self):
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return (
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f"n_fft={self.n_fft}, "
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f"win_length={self.win_length}, "
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f"hop_length={self.hop_length}, "
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f"center={self.center}, "
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f"pad_mode={self.pad_mode}, "
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f"normalized={self.normalized}, "
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f"onesided={self.onesided}"
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)
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def forward(
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self, input: torch.Tensor, ilens: torch.Tensor = None
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""STFT forward function.
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Args:
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input: (Batch, Nsamples) or (Batch, Nsample, Channels)
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ilens: (Batch)
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Returns:
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output: (Batch, Frames, Freq, 2) or (Batch, Frames, Channels, Freq, 2)
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"""
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bs = input.size(0)
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if input.dim() == 3:
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multi_channel = True
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# input: (Batch, Nsample, Channels) -> (Batch * Channels, Nsample)
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input = input.transpose(1, 2).reshape(-1, input.size(1))
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else:
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multi_channel = False
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# output: (Batch, Freq, Frames, 2=real_imag)
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# or (Batch, Channel, Freq, Frames, 2=real_imag)
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if not self.kaldi_padding_mode:
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output = torch.stft(
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input,
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n_fft=self.n_fft,
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win_length=self.win_length,
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hop_length=self.hop_length,
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center=self.center,
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pad_mode=self.pad_mode,
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normalized=self.normalized,
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onesided=self.onesided,
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return_complex=False
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)
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else:
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# NOTE(sx): Use Kaldi-fasion padding, maybe wrong
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num_pads = self.n_fft - self.win_length
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input = torch.nn.functional.pad(input, (num_pads, 0))
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output = torch.stft(
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input,
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n_fft=self.n_fft,
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win_length=self.win_length,
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hop_length=self.hop_length,
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center=False,
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pad_mode=self.pad_mode,
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normalized=self.normalized,
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onesided=self.onesided,
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return_complex=False
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)
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# output: (Batch, Freq, Frames, 2=real_imag)
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# -> (Batch, Frames, Freq, 2=real_imag)
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output = output.transpose(1, 2)
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if multi_channel:
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# output: (Batch * Channel, Frames, Freq, 2=real_imag)
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# -> (Batch, Frame, Channel, Freq, 2=real_imag)
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output = output.view(bs, -1, output.size(1), output.size(2), 2).transpose(
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1, 2
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)
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if ilens is not None:
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if self.center:
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pad = self.win_length // 2
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ilens = ilens + 2 * pad
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olens = torch.div(ilens - self.win_length, self.hop_length, rounding_mode='floor') + 1
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# olens = ilens - self.win_length // self.hop_length + 1
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output.masked_fill_(make_pad_mask(olens, output, 1), 0.0)
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else:
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olens = None
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return output, olens
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