---
title: Short-Time Objective Intelligibility (STOI)
description: >-
  Calculate STOI (Short-Time Objective Intelligibility) metric for evaluating
  speech signals.
canonical: >-
  https://lightning.ai/docs/torchmetrics/stable/audio/short_time_objective_intelligibility
---
# Short-Time Objective Intelligibility (STOI) {#short-time-objective-intelligibility-stoi}

## Module Interface {#module-interface}

### `class torchmetrics.audio.stoi.ShortTimeObjectiveIntelligibility(fs, extended=False, **kwargs)` {#torchmetrics.audio.stoi.ShortTimeObjectiveIntelligibility}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/audio/stoi.py#L30-L159)

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](http://www.ceestaal.nl/code/) and for further details see [STOI ref1](https://ieeexplore.ieee.org/abstract/document/5495701) and [STOI ref2](https://ieeexplore.ieee.org/abstract/document/5713237).

This metric is a wrapper for the [pystoi package](https://github.com/mpariente/pystoi). 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`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): float tensor with shape `(...,time)`
    
*   `target` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): float tensor with shape `(...,time)`
    

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

*   `stoi` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): float scalar tensor
    

Hint

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

Parameters:

*   **fs** ([`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")) – sampling frequency (Hz)
    
*   **extended** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – whether to use the extended STOI described in [STOI ref3](https://ieeexplore.ieee.org/abstract/document/7539284).
    
*   **kwargs** ([`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")) – Additional keyword arguments, see [Advanced metric settings](/docs/torchmetrics/1.9.0/user-guide/overview.md#metric-kwargs) for more info.
    

Raises:

[**ModuleNotFoundError**](https://docs.python.org/3/builtins/exceptions.html#ModuleNotFoundError "(in Python v3.15)") – If `pystoi` package is not installed

Example

```python
>>> from torch import randn
>>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility
>>> preds = randn(8000)
>>> target = randn(8000)
>>> stoi = ShortTimeObjectiveIntelligibility(8000, False)
>>> stoi(preds, target)
tensor(-0.084...)
```

#### `compute()` {#torchmetrics.audio.stoi.ShortTimeObjectiveIntelligibility.compute}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/audio/stoi.py#L113-L115)

Compute metric.

Return type:

[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")

#### `plot(val=None, ax=None)` {#torchmetrics.audio.stoi.ShortTimeObjectiveIntelligibility.plot}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/audio/stoi.py#L117-L159)

Plot a single or multiple values from the metric.

Parameters:

*   **val** (`Union`\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)"), [`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")\], [`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")\]) – 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`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)")\]) – An matplotlib axis object. If provided will add plot to that axis
    

Return type:

[`tuple`](https://docs.python.org/3/builtins/stdtypes.html#tuple "(in Python v3.15)")\[[`Figure`](https://matplotlib.org/stable/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure "(in Matplotlib v3.11.2)"), `Union`\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)"), [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v2.5)")\]\]

Returns:

Figure and Axes object

Raises:

[**ModuleNotFoundError**](https://docs.python.org/3/builtins/exceptions.html#ModuleNotFoundError "(in Python v3.15)") – If matplotlib is not installed

```python
>>> # Example plotting a single value
>>> from torch import randn
>>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility
>>> preds = randn(8000)
>>> target = randn(8000)
>>> metric = ShortTimeObjectiveIntelligibility(8000, False)
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
```

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

#### `update(preds, target)` {#torchmetrics.audio.stoi.ShortTimeObjectiveIntelligibility.update}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/audio/stoi.py#L104-L111)

Update state with predictions and targets.

Return type:

[`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")

## Functional Interface {#functional-interface}

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

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/functional/audio/stoi.py#L25-L95)

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](http://www.ceestaal.nl/code/) and for further details see [STOI ref1](https://ieeexplore.ieee.org/abstract/document/5495701) and [STOI ref2](https://ieeexplore.ieee.org/abstract/document/5713237).

This metric is a wrapper for the [pystoi package](https://github.com/mpariente/pystoi). 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.

Hint

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

Parameters:

*   **preds** ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")) – float tensor with shape `(...,time)`
    
*   **target** ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")) – float tensor with shape `(...,time)`
    
*   **fs** ([`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")) – sampling frequency (Hz)
    
*   **extended** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – whether to use the extended STOI described in [STOI ref3](https://ieeexplore.ieee.org/abstract/document/7539284).
    
*   **keep\_same\_device** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – whether to move the stoi value to the device of preds
    

Return type:

[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")

Returns:

stoi value of shape \[…\]

Raises:

*   [**ModuleNotFoundError**](https://docs.python.org/3/builtins/exceptions.html#ModuleNotFoundError "(in Python v3.15)") – If `pystoi` package is not installed
    
*   [**RuntimeError**](https://docs.python.org/3/builtins/exceptions.html#RuntimeError "(in Python v3.15)") – If `preds` and `target` does not have the same shape
    

Example

```python
>>> from torch import randn
>>> from torchmetrics.functional.audio.stoi import short_time_objective_intelligibility
>>> preds = randn(8000)
>>> target = randn(8000)
>>> short_time_objective_intelligibility(preds, target, 8000).float()
tensor(-0.084...)
```

---

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- Parent: [Audio](/docs/torchmetrics/1.9.0/audio.md)