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

Module Interface¶

class torchmetrics.ScaleInvariantSignalNoiseRatio(**kwargs)[source]

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

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

Functional Interface¶

torchmetrics.functional.scale_invariant_signal_noise_ratio(preds, target)[source]

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)