Short-Time Objective Intelligibility (STOI)

Module Interface

class torchmetrics.audio.stoi.ShortTimeObjectiveIntelligibility(fs, extended=False, **kwargs)

Calculate STOI (Short-Time Objective Intelligibility) metric for evaluating speech signals.

Intelligibility measure which is highly correlated with the intelligibility of degraded speech signals, e.g., due to additive noise, single-/multi-channel noise reduction, binary masking and vocoded speech as in CI simulations. The STOI-measure is intrusive, i.e., a function of the clean and degraded speech signals. STOI may be a good alternative to the speech intelligibility index (SII) or the speech transmission index (STI), when you are interested in the effect of nonlinear processing to noisy speech, e.g., noise reduction, binary masking algorithms, on speech intelligibility. Description taken from Cees Taal’s website and for further defails see STOI ref1 and STOI ref2.

This metric is a wrapper for the pystoi package. As the implementation backend implementation only supports calculations on CPU, all input will automatically be moved to CPU to perform the metric calculation before being moved back to the original device.

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

• stoi (Tensor): float scalar tensor

Note

using this metrics requires you to have pystoi install. Either install as pip install torchmetrics[audio] or pip install pystoi.

Parameters

• fs (int) – sampling frequency (Hz)

• extended (bool) – whether to use the extended STOI described in STOI ref3.

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

Raises

ModuleNotFoundError – If pystoi package is not installed

Example

>>> import torch
>>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility
>>> g = torch.manual_seed(1)
>>> preds = torch.randn(8000)
>>> target = torch.randn(8000)
>>> stoi = ShortTimeObjectiveIntelligibility(8000, False)
>>> stoi(preds, target)
tensor(-0.0100)

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

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 ShortTimeObjectiveIntelligibility
>>> g = torch.manual_seed(1)
>>> preds = torch.randn(8000)
>>> target = torch.randn(8000)
>>> metric = ShortTimeObjectiveIntelligibility(8000, False)
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()

(Source code, png, hires.png, pdf)

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility
>>> metric = ShortTimeObjectiveIntelligibility(8000, False)
>>> g = torch.manual_seed(1)
>>> preds = torch.randn(8000)
>>> target = torch.randn(8000)
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)

(Source code, png, hires.png, pdf)

Functional Interface

torchmetrics.functional.audio.stoi.short_time_objective_intelligibility(preds, target, fs, extended=False, keep_same_device=False)

Calculate STOI (Short-Time Objective Intelligibility) metric for evaluating speech signals.

Intelligibility measure which is highly correlated with the intelligibility of degraded speech signals, e.g., due to additive noise, single-/multi-channel noise reduction, binary masking and vocoded speech as in CI simulations. The STOI-measure is intrusive, i.e., a function of the clean and degraded speech signals. STOI may be a good alternative to the speech intelligibility index (SII) or the speech transmission index (STI), when you are interested in the effect of nonlinear processing to noisy speech, e.g., noise reduction, binary masking algorithms, on speech intelligibility. Description taken from Cees Taal’s website and for further defails see STOI ref1 and STOI ref2.

This metric is a wrapper for the pystoi package. As the implementation backend implementation only supports calculations on CPU, all input will automatically be moved to CPU to perform the metric calculation before being moved back to the original device.

Note

using this metrics requires you to have pystoi install. Either install as pip install torchmetrics[audio] or pip install pystoi

Parameters

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

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

• fs (int) – sampling frequency (Hz)

• extended (bool) – whether to use the extended STOI described in STOI ref3.

• keep_same_device (bool) – whether to move the stoi value to the device of preds

Return type

Tensor

Returns

stoi value of shape […]

Raises

• ModuleNotFoundError – If pystoi package is not installed

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

Example

>>> import torch
>>> from torchmetrics.functional.audio.stoi import short_time_objective_intelligibility
>>> g = torch.manual_seed(1)
>>> preds = torch.randn(8000)
>>> target = torch.randn(8000)
>>> short_time_objective_intelligibility(preds, target, 8000).float()
tensor(-0.0100)