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Scale-Invariant Signal-to-Distortion Ratio (SI-SDR)

Module Interface

class torchmetrics.ScaleInvariantSignalDistortionRatio(zero_mean=False, **kwargs)[source]

Scale-invariant signal-to-distortion ratio (SI-SDR). The SI-SDR value is in general considered an overall measure of how good a source sound.

As input to forward and update the metric accepts the following input

  • preds (Tensor): float tensor with shape (...,time)

  • target (: Tensor): float tensor with shape (...,time)

As output of forward and compute the metric returns the following output

  • si_sdr (: Tensor): float scalar tensor with average SI-SDR value over samples

Parameters
Raises

TypeError – if target and preds have a different shape

Example

>>> from torch import tensor
>>> from torchmetrics import ScaleInvariantSignalDistortionRatio
>>> target = tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = tensor([2.5, 0.0, 2.0, 8.0])
>>> si_sdr = ScaleInvariantSignalDistortionRatio()
>>> si_sdr(preds, target)
tensor(18.4030)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface

torchmetrics.functional.scale_invariant_signal_distortion_ratio(preds, target, zero_mean=False)[source]

Scale-invariant signal-to-distortion ratio (SI-SDR). The SI-SDR value is in general considered an overall measure of how good a source sound.

Parameters
  • preds (Tensor) – float tensor with shape (...,time)

  • target (Tensor) – float tensor with shape (...,time)

  • zero_mean (bool) – If to zero mean target and preds or not

Return type

Tensor

Returns

Float tensor with shape (...,) of SDR values per sample

Raises

RuntimeError – If preds and target does not have the same shape

Example

>>> from torchmetrics.functional.audio import scale_invariant_signal_distortion_ratio
>>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
>>> scale_invariant_signal_distortion_ratio(preds, target)
tensor(18.4030)
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