Source code for diffsptk.core.gnorm

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# Copyright 2022 SPTK Working Group                                        #
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import torch
import torch.nn as nn

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


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

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

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

    c : int >= 1 [scalar]
        Number of stages.

    """

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

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

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

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

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

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

        Examples
        --------
        >>> x = diffsptk.ramp(1, 4)
        >>> gnorm = diffsptk.GeneralizedCepstrumGainNormalization(3, c=2)
        >>> y = gnorm(x)
        >>> y
        tensor([2.2500, 1.3333, 2.0000, 2.6667])

        """
        check_size(x.size(-1), self.cep_order + 1, "dimension of cepstrum")

        x0, x1 = torch.split(x, [1, self.cep_order], dim=-1)
        if self.gamma == 0:
            K = torch.exp(x0)
            y = x1
        else:
            z = 1 + self.gamma * x0
            K = torch.pow(z, 1 / self.gamma)
            y = x1 / z

        y = torch.cat((K, y), dim=-1)
        return y