Source code for diffsptk.modules.b2mc

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# Copyright 2022 SPTK Working Group                                        #
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import torch
from torch import nn
import torch.nn.functional as F

from ..misc.utils import check_size
from ..misc.utils import to




class MLSADigitalFilterCoefficientsToMelCepstrum(nn.Module):
    """See `this page <https://sp-nitech.github.io/sptk/latest/main/b2mc.html>`_
    for details.

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

    alpha : float in (-1, 1)
        Frequency warping factor, :math:`\\alpha`.

    """

    def __init__(self, cep_order, alpha=0):
        super().__init__()

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

        self.cep_order = cep_order
        self.alpha = alpha

        # Make transform matrix.
        A = torch.eye(self.cep_order + 1, dtype=torch.double)
        A[:, 1:].fill_diagonal_(self.alpha)
        self.register_buffer("A", to(A.T))



    def forward(self, b):
        """Convert MLSA filter coefficients to mel-cepstrum.

        Parameters
        ----------
        b : Tensor [shape=(..., M+1)]
            MLSA filter coefficients.

        Returns
        -------
        out : Tensor [shape=(..., M+1)]
            Mel-cepstral coefficients.

        Examples
        --------
        >>> b = diffsptk.ramp(4)
        >>> mc2b = diffsptk.MelCepstrumToMLSADigitalFilterCoefficients(4, 0.3)
        >>> b2mc = diffsptk.MLSADigitalFilterCoefficientsToMelCepstrum(4, 0.3)
        >>> b2 = mc2b(b2mc(b))
        >>> b2
        tensor([0.0000, 1.0000, 2.0000, 3.0000, 4.0000])

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


    @staticmethod
    def _forward(b, A):
        return torch.matmul(b, A)

    @staticmethod
    def _func(b, alpha):
        return b + F.pad(alpha * b[..., 1:], (0, 1))