Source code for diffsptk.modules.imdst

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# Copyright 2022 SPTK Working Group                                        #
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import torch
from torch import nn

from ..typing import Precomputed
from ..utils.private import get_values
from .base import BaseFunctionalModule
from .imdct import InverseModifiedDiscreteCosineTransform as IMDCT




class InverseModifiedDiscreteSineTransform(BaseFunctionalModule):
    """This is the opposite module to :func:`~diffsptk.ModifiedDiscreteSineTransform`.

    Parameters
    ----------
    frame_length : int >= 2
        The frame length, :math:`L`.

    window : ['sine', 'vorbis', 'kbd', 'rectangular']
        The window type.

    """

    def __init__(self, frame_length: int, window: str = "sine"):
        super().__init__()

        self.values, layers, _ = self._precompute(*get_values(locals()))
        self.layers = nn.ModuleList(layers)



    def forward(self, y: torch.Tensor, out_length: int | None = None) -> torch.Tensor:
        """Compute inverse modified discrete sine transform.

        Parameters
        ----------
        y : Tensor [shape=(..., 2T/L, L/2)]
            The spectrum.

        out_length : int or None
            The length of the output waveform.

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

        Examples
        --------
        >>> x = diffsptk.ramp(3)
        >>> x
        tensor([0., 1., 2., 3.])
        >>> mdst_params = {"frame_length": 4}
        >>> mdst = diffsptk.MDST(**mdst_params)
        >>> imdst = diffsptk.IMDST(**mdst_params)
        >>> y = imdst(mdst(x))
        >>> y
        tensor([-8.9407e-08, 1.0000e+00, 2.0000e+00, 3.0000e+00])

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


    @staticmethod
    def _func(*args, **kwargs) -> torch.Tensor:
        return IMDCT._func(*args, **kwargs, transform="sine")

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

    @staticmethod
    def _check(*args, **kwargs) -> None:
        raise NotImplementedError

    @staticmethod
    def _precompute(frame_length: int, window: str) -> Precomputed:
        return IMDCT._precompute(frame_length, window, transform="sine")

    @staticmethod
    def _forward(*args, **kwargs) -> torch.Tensor:
        return IMDCT._forward(*args, **kwargs)