Shortcuts

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

Module Interface

class torchmetrics.audio.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.audio 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)
plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
  • val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

  • ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

Tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.audio import ScaleInvariantSignalDistortionRatio
>>> target = torch.randn(5)
>>> preds = torch.randn(5)
>>> metric = ScaleInvariantSignalDistortionRatio()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
../_images/scale_invariant_signal_distortion_ratio-1.png
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio import ScaleInvariantSignalDistortionRatio
>>> target = torch.randn(5)
>>> preds = torch.randn(5)
>>> metric = ScaleInvariantSignalDistortionRatio()
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)
../_images/scale_invariant_signal_distortion_ratio-2.png

Functional Interface

torchmetrics.functional.audio.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)
Read the Docs v: stable
Versions
latest
stable
v1.1.0
v1.0.3
v1.0.2
v1.0.1
v1.0.0
v0.11.4
v0.11.3
v0.11.2
v0.11.1
v0.11.0
v0.10.3
v0.10.2
v0.10.1
v0.10.0
v0.9.3
v0.9.2
v0.9.1
v0.9.0
v0.8.2
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0
Downloads
pdf
html
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.