| 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.]
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| (default: {torch.finfo(torch.float64).eps})
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| top_db {[float]} -- [threshold the output at ``top_db`` below the peak]
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| ` (default: {40})
|
|
|
| Returns:
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| [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.]
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| 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.]
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| stop {[Number]} -- [The end value of the sequence, unless `endpoint` is set to False.
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| 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:
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| num {[int]} -- [Number of samples to generate. Default is 50. Must be non-negative.]
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| endpoint {[bool]} -- [If True, `stop` is the last sample. Otherwise, it is not included.
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| Default is True.]
|
| **kwargs -- [Additional arguments to be passed to the underlying PyTorch `linspace` function.]
|
|
|
| Returns:
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| [torch.Tensor] -- [1-D tensor of `num` equally spaced samples from `start` to `stop`.]
|
| """
|
| if endpoint:
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| return torch.linspace(start, stop, num, **kwargs)
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| else:
|
| return torch.linspace(start, stop, num + 1, **kwargs)[:-1]
|
|
|
