class_docstr: 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 details 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`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``
  - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``stoi`` (:class:`~torch.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``.
  
  Args:
      fs: sampling frequency (Hz)
      extended: whether to use the extended STOI described in `STOI ref3`_.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` 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)
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docstr: Update state with predictions and targets.
docstr_tok: ["Update","state","with","predictions","and","targets","."]
code: def update(self, preds: Tensor, target: Tensor) -> None:
      """Update state with predictions and targets."""
      stoi_batch = short_time_objective_intelligibility(
          preds, target, self.fs, self.extended, False
      ).to(self.sum_stoi.device)
  
      self.sum_stoi += stoi_batch.sum()
      self.total += stoi_batch.numel()
  
file_path: torchmetrics/src/torchmetrics/audio/stoi.py
imports: from typing import Any, Optional, Sequence, Union
  from torch import Tensor, tensor
  from torchmetrics.functional.audio.stoi import short_time_objective_intelligibility
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _PYSTOI_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 2 for calling the ShortTimeObjectiveIntelligibility obj's underlying member method code with example usage: obj.update(preds, target) without return types