Source code for diffsptk.modules.ulaw

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# Copyright 2022 SPTK Working Group                                        #
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import math

import torch

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




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

    Parameters
    ----------
    abs_max : float > 0
        The absolute maximum value of the 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, x: torch.Tensor) -> torch.Tensor:
        """Compress the input waveform using the :math:`\\mu`-law algorithm.

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

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

        Examples
        --------
        >>> x = diffsptk.ramp(4)
        >>> ulaw = diffsptk.MuLawCompression(4)
        >>> y = ulaw(x)
        >>> y
        tensor([0.0000, 3.0084, 3.5028, 3.7934, 4.0000])

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


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

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

    @staticmethod
    def _check(abs_max: float, mu: int) -> None:
        if abs_max < 0:
            raise ValueError("abs_max must be non-negative.")
        if mu < 1:
            raise ValueError("mu must be greater than or equal to 1.")

    @staticmethod
    def _precompute(abs_max: float, mu: int) -> Precomputed:
        MuLawCompression._check(abs_max, mu)
        return (
            abs_max,
            mu,
            abs_max / math.log1p(mu),
        )

    @staticmethod
    def _forward(x: torch.Tensor, abs_max: float, mu: int, c: float) -> torch.Tensor:
        x_abs = x.abs() / abs_max
        y = c * torch.sign(x) * torch.log1p(mu * x_abs)
        return y