Source code for diffsptk.modules.ipnorm

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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, get_values
from .base import BaseFunctionalModule



[docs]
class MelCepstrumInversePowerNormalization(BaseFunctionalModule):
    """See `this page <https://sp-nitech.github.io/sptk/latest/main/ipnorm.html>`_
    for details.

    Parameters
    ----------
    cep_order : int >= 0
        The order of the cepstrum, :math:`M`.

    """

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

        self.in_dim = cep_order + 2

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


[docs]
    def forward(self, y: torch.Tensor) -> torch.Tensor:
        """Perform mel-cepstrum inverse power normalization.

        Parameters
        ----------
        y : Tensor [shape=(..., M+2)]
            The log power and power-normalized cepstrum.

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

        Examples
        --------
        >>> x = diffsptk.ramp(1, 4)
        >>> pnorm = diffsptk.MelCepstrumPowerNormalization(3, alpha=0.1)
        >>> ipnorm = diffsptk.MelCepstrumInversePowerNormalization(3)
        >>> y = ipnorm(pnorm(x))
        >>> y
        tensor([1., 2., 3., 4.])

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


    @staticmethod
    def _func(y: torch.Tensor, *args, **kwargs) -> torch.Tensor:
        MelCepstrumInversePowerNormalization._precompute(
            y.size(-1) - 1, *args, **kwargs
        )
        return MelCepstrumInversePowerNormalization._forward(y)

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

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

    @staticmethod
    def _precompute(cep_order: int) -> Precomputed:
        MelCepstrumInversePowerNormalization._check(cep_order)
        return (None,)

    @staticmethod
    def _forward(y: torch.Tensor) -> torch.Tensor:
        P, y1, y2 = torch.split(y, [1, 1, y.size(-1) - 2], dim=-1)
        x = torch.cat((0.5 * P + y1, y2), dim=-1)
        return x