Source code for diffsptk.modules.lar2par

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# Copyright 2022 SPTK Working Group                                        #
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import torch

from ..typing import Precomputed
from ..utils.private import check_size, filter_values
from .base import BaseFunctionalModule




class LogAreaRatioToParcorCoefficients(BaseFunctionalModule):
    """See `this page <https://sp-nitech.github.io/sptk/latest/main/lar2par.html>`_
    for details.

    Parameters
    ----------
    par_order : int >= 0
        The order of the PARCOR coefficients, :math:`M`.

    """

    def __init__(self, par_order: int) -> None:
        super().__init__()

        self.in_dim = par_order + 1

        self.values = self._precompute(**filter_values(locals()))



    def forward(self, g: torch.Tensor) -> torch.Tensor:
        """Convert LAR to PARCOR.

        Parameters
        ----------
        g : Tensor [shape=(..., M+1)]
            The log area ratio.

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

        Examples
        --------
        >>> import diffsptk
        >>> lar2par = diffsptk.LogAreaRatioToParcorCoefficients(3)
        >>> g = diffsptk.ramp(1, 4) * 0.1
        >>> k = lar2par(g)
        >>> k
        tensor([0.1000, 0.0997, 0.1489, 0.1974])

        """
        check_size(g.size(-1), self.in_dim, "dimension of parcor")
        return self._forward(g, *self.values)


    @staticmethod
    def _func(x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
        values = LogAreaRatioToParcorCoefficients._precompute(
            x.size(-1) - 1, *args, **kwargs
        )
        return LogAreaRatioToParcorCoefficients._forward(x, *values)

    @staticmethod
    def _takes_input_size() -> bool:
        return True

    @staticmethod
    def _check(par_order: int) -> None:
        if par_order < 0:
            raise ValueError("par_order must be non-negative.")

    @staticmethod
    def _precompute(par_order: int) -> Precomputed:
        LogAreaRatioToParcorCoefficients._check(par_order)
        return (0.5,)

    @staticmethod
    def _forward(g: torch.Tensor, c: float) -> torch.Tensor:
        K, g = torch.split(g, [1, g.size(-1) - 1], dim=-1)
        k = torch.cat((K, torch.tanh(c * g)), dim=-1)
        return k