| | 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] |
| |
|
