Source code for diffsptk.modules.ignorm

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# Copyright 2022 SPTK Working Group                                        #
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import torch
from torch import nn

from ..misc.utils import check_size
from ..misc.utils import get_gamma




class GeneralizedCepstrumInverseGainNormalization(nn.Module):
    """See `this page <https://sp-nitech.github.io/sptk/latest/main/ignorm.html>`_
    for details.

    Parameters
    ----------
    cep_order : int >= 0
        Order of cepstrum, :math:`M`.

    gamma : float in [-1, 1]
        Gamma, :math:`\\gamma`.

    c : int >= 1 or None
        Number of stages.

    """

    def __init__(self, cep_order, gamma=0, c=None):
        super().__init__()

        assert 0 <= cep_order
        assert abs(gamma) <= 1

        self.cep_order = cep_order
        self.gamma = self._precompute(gamma, c)



    def forward(self, y):
        """Perform cepstrum inverse gain normalization.

        Parameters
        ----------
        y : Tensor [shape=(..., M+1)]
            Normalized generalized cepstrum.

        Returns
        -------
        x : Tensor [shape=(..., M+1)]
            Generalized cepstrum.

        Examples
        --------
        >>> x = diffsptk.ramp(1, 4)
        >>> gnorm = diffsptk.GeneralizedCepstrumGainNormalization(3, c=2)
        >>> ignorm = diffsptk.GeneralizedCepstrumInverseGainNormalization(3, c=2)
        >>> x2 = ignorm(gnorm(x))
        >>> x2
        tensor([1., 2., 3., 4.])

        """
        check_size(y.size(-1), self.cep_order + 1, "dimension of cepstrum")
        return self._forward(y, self.gamma)


    @staticmethod
    def _forward(y, gamma):
        K, y = torch.split(y, [1, y.size(-1) - 1], dim=-1)
        if gamma == 0:
            x0 = torch.log(K)
            x1 = y
        else:
            z = torch.pow(K, gamma)
            x0 = (z - 1) / gamma
            x1 = y * z
        x = torch.cat((x0, x1), dim=-1)
        return x

    @staticmethod
    def _func(y, gamma, c=None):
        gamma = GeneralizedCepstrumInverseGainNormalization._precompute(gamma, c)
        return GeneralizedCepstrumInverseGainNormalization._forward(y, gamma)

    @staticmethod
    def _precompute(gamma, c):
        return get_gamma(gamma, c)