Scale-Invariant Signal-to-Noise Ratio (SI-SNR)

Module Interface

class torchmetrics.audio.ScaleInvariantSignalNoiseRatio(**kwargs)

Calculate Scale-invariant signal-to-noise ratio (SI-SNR) metric for evaluating quality of audio.

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_snr (Tensor): float scalar tensor with average SI-SNR value over samples

Parameters:

kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Raises:

TypeError – if target and preds have a different shape

Example

>>> import torch
>>> from torch import tensor
>>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
>>> target = tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = tensor([2.5, 0.0, 2.0, 8.0])
>>> si_snr = ScaleInvariantSignalNoiseRatio()
>>> si_snr(preds, target)
tensor(15.0918)

plot(val=None, ax=None)

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 ScaleInvariantSignalNoiseRatio
>>> metric = ScaleInvariantSignalNoiseRatio()
>>> metric.update(torch.rand(4), torch.rand(4))
>>> fig_, ax_ = metric.plot()

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
>>> metric = ScaleInvariantSignalNoiseRatio()
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(torch.rand(4), torch.rand(4)))
>>> fig_, ax_ = metric.plot(values)

Functional Interface

torchmetrics.functional.audio.scale_invariant_signal_noise_ratio(preds, target)

Scale-invariant signal-to-noise ratio (SI-SNR).

Parameters:

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

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

Return type:

Tensor

Returns:

Float tensor with shape (...,) of SI-SNR values per sample

Raises:

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

Example

>>> import torch
>>> from torchmetrics.functional.audio import scale_invariant_signal_noise_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_noise_ratio(preds, target)
tensor(15.0918)