Retrieval-based-Voice-Conversion-WebUI/tools/torchgate/utils.py

import torch
from torch.types import Number


@torch.no_grad()
def amp_to_db(
    x: torch.Tensor, eps=torch.finfo(torch.float64).eps, top_db=40
) -> torch.Tensor:
    """
    Convert the input tensor from amplitude to decibel scale.

    Arguments:
        x {[torch.Tensor]} -- [Input tensor.]

    Keyword Arguments:
        eps {[float]} -- [Small value to avoid numerical instability.]
                          (default: {torch.finfo(torch.float64).eps})
        top_db {[float]} -- [threshold the output at ``top_db`` below the peak]
            `             (default: {40})

    Returns:
        [torch.Tensor] -- [Output tensor in decibel scale.]
    """
    x_db = 20 * torch.log10(x.abs() + eps)
    return torch.max(x_db, (x_db.max(-1).values - top_db).unsqueeze(-1))


@torch.no_grad()
def temperature_sigmoid(x: torch.Tensor, x0: float, temp_coeff: float) -> torch.Tensor:
    """
    Apply a sigmoid function with temperature scaling.

    Arguments:
        x {[torch.Tensor]} -- [Input tensor.]
        x0 {[float]} -- [Parameter that controls the threshold of the sigmoid.]
        temp_coeff {[float]} -- [Parameter that controls the slope of the sigmoid.]

    Returns:
        [torch.Tensor] -- [Output tensor after applying the sigmoid with temperature scaling.]
    """
    return torch.sigmoid((x - x0) / temp_coeff)


@torch.no_grad()
def linspace(
    start: Number, stop: Number, num: int = 50, endpoint: bool = True, **kwargs
) -> torch.Tensor:
    """
    Generate a linearly spaced 1-D tensor.

    Arguments:
        start {[Number]} -- [The starting value of the sequence.]
        stop {[Number]} -- [The end value of the sequence, unless `endpoint` is set to False.
                            In that case, the sequence consists of all but the last of ``num + 1``
                            evenly spaced samples, so that `stop` is excluded. Note that the step
                            size changes when `endpoint` is False.]

    Keyword Arguments:
        num {[int]} -- [Number of samples to generate. Default is 50. Must be non-negative.]
        endpoint {[bool]} -- [If True, `stop` is the last sample. Otherwise, it is not included.
                              Default is True.]
        **kwargs -- [Additional arguments to be passed to the underlying PyTorch `linspace` function.]

    Returns:
        [torch.Tensor] -- [1-D tensor of `num` equally spaced samples from `start` to `stop`.]
    """
    if endpoint:
        return torch.linspace(start, stop, num, **kwargs)
    else:
        return torch.linspace(start, stop, num + 1, **kwargs)[:-1]