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import torch |
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from torch.types import Number |
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@torch.no_grad() |
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def amp_to_db(x: torch.Tensor, eps=torch.finfo(torch.float64).eps, top_db=40) -> torch.Tensor: |
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""" |
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Convert the input tensor from amplitude to decibel scale. |
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Arguments: |
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x {[torch.Tensor]} -- [Input tensor.] |
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Keyword Arguments: |
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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}) |
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Returns: |
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[torch.Tensor] -- [Output tensor in decibel scale.] |
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""" |
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x_db = 20 * torch.log10(x.abs() + eps) |
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return torch.max(x_db, (x_db.max(-1).values - top_db).unsqueeze(-1)) |
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@torch.no_grad() |
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def temperature_sigmoid(x: torch.Tensor, x0: float, temp_coeff: float) -> torch.Tensor: |
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""" |
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Apply a sigmoid function with temperature scaling. |
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Arguments: |
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x {[torch.Tensor]} -- [Input tensor.] |
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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.] |
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Returns: |
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[torch.Tensor] -- [Output tensor after applying the sigmoid with temperature scaling.] |
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""" |
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return torch.sigmoid((x - x0) / temp_coeff) |
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@torch.no_grad() |
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def linspace(start: Number, stop: Number, num: int = 50, endpoint: bool = True, **kwargs) -> torch.Tensor: |
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""" |
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Generate a linearly spaced 1-D tensor. |
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Arguments: |
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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`` |
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evenly spaced samples, so that `stop` is excluded. Note that the step |
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size changes when `endpoint` is False.] |
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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.] |
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**kwargs -- [Additional arguments to be passed to the underlying PyTorch `linspace` function.] |
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Returns: |
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[torch.Tensor] -- [1-D tensor of `num` equally spaced samples from `start` to `stop`.] |
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""" |
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if endpoint: |
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return torch.linspace(start, stop, num, **kwargs) |
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else: |
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return torch.linspace(start, stop, num + 1, **kwargs)[:-1] |
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