Source code for diffsptk.modules.iulaw

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# Copyright 2022 SPTK Working Group                                        #
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import torch

from ..typing import Precomputed
from ..utils.private import filter_values
from .base import BaseFunctionalModule
from .ulaw import MuLawCompression




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

    Parameters
    ----------
    abs_max : float > 0
        The absolute maximum value of the original input waveform.

    mu : int >= 1
        The compression factor, :math:`\\mu`.

    """

    def __init__(self, abs_max: float = 1, mu: int = 255) -> None:
        super().__init__()

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



    def forward(self, y: torch.Tensor) -> torch.Tensor:
        """Expand the waveform using the :math:`\\mu`-law algorithm.

        Parameters
        ----------
        y : Tensor [shape=(...,)]
            The input compressed waveform.

        Returns
        -------
        out : Tensor [shape=(...,)]
            The expanded waveform.

        Examples
        --------
        >>> import diffsptk
        >>> ulaw = diffsptk.MuLawCompression(4)
        >>> iulaw = diffsptk.MuLawExpansion(4)
        >>> x = diffsptk.ramp(4)
        >>> x2 = iulaw(ulaw(x))
        >>> x2
        tensor([0.0000, 1.0000, 2.0000, 3.0000, 4.0000])

        """
        return self._forward(y, *self.values)


    @staticmethod
    def _func(y: torch.Tensor, *args, **kwargs) -> torch.Tensor:
        values = MuLawExpansion._precompute(*args, **kwargs)
        return MuLawExpansion._forward(y, *values)

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

    @staticmethod
    def _check(*args, **kwargs) -> None:
        MuLawCompression._check(*args, **kwargs)

    @staticmethod
    def _precompute(abs_max: float, mu: int) -> Precomputed:
        MuLawExpansion._check(abs_max, mu)
        return (
            abs_max,
            mu,
            abs_max / mu,
        )

    @staticmethod
    def _forward(y: torch.Tensor, abs_max: float, mu: int, c: float) -> torch.Tensor:
        y_abs = y.abs() / abs_max
        x = c * torch.sign(y) * (torch.pow(1 + mu, y_abs) - 1)
        return x