class_docstr: Calculate `Speech-to-Reverberation Modulation Energy Ratio`_ (SRMR).
  
  SRMR is a non-intrusive metric for speech quality and intelligibility based on
  a modulation spectral representation of the speech signal.
  This code is translated from `SRMRToolbox`_ and `SRMRpy`_.
  
  As input to ``forward`` and ``update`` the metric accepts the following input
  
  - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``srmr`` (:class:`~torch.Tensor`): float scaler tensor
  
  .. note:: using this metrics requires you to have ``gammatone`` and ``torchaudio`` installed.
      Either install as ``pip install torchmetrics[audio]`` or ``pip install torchaudio``
      and ``pip install git+https://github.com/detly/gammatone``.
  
  .. note::
      This implementation is experimental, and might not be consistent with the matlab
      implementation `SRMRToolbox`_, especially the fast implementation.
      The slow versions, a) fast=False, norm=False, max_cf=128, b) fast=False, norm=True, max_cf=30, have
      a relatively small inconsistence.
  
  Args:
      fs: the sampling rate
      n_cochlear_filters: Number of filters in the acoustic filterbank
      low_freq: determines the frequency cutoff for the corresponding gammatone filterbank.
      min_cf: Center frequency in Hz of the first modulation filter.
      max_cf: Center frequency in Hz of the last modulation filter. If None is given,
          then 30 Hz will be used for `norm==False`, otherwise 128 Hz will be used.
      norm: Use modulation spectrum energy normalization
      fast: Use the faster version based on the gammatonegram.
          Note: this argument is inherited from `SRMRpy`_. As the translated code is based to pytorch,
          setting `fast=True` may slow down the speed for calculating this metric on GPU.
  
  Raises:
      ModuleNotFoundError:
          If ``gammatone`` or ``torchaudio`` package is not installed
  
  Example:
      >>> import torch
      >>> from torchmetrics.audio import SpeechReverberationModulationEnergyRatio
      >>> g = torch.manual_seed(1)
      >>> preds = torch.randn(8000)
      >>> srmr = SpeechReverberationModulationEnergyRatio(8000)
      >>> srmr(preds)
      tensor(0.3354)
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docstr: Update state with predictions.
docstr_tok: ["Update","state","with","predictions","."]
code: def update(self, preds: Tensor) -> None:
      """Update state with predictions."""
      metric_val_batch = speech_reverberation_modulation_energy_ratio(
          preds,
          self.fs,
          self.n_cochlear_filters,
          self.low_freq,
          self.min_cf,
          self.max_cf,
          self.norm,
          self.fast,
      ).to(self.msum.device)
  
      self.msum += metric_val_batch.sum()
      self.total += metric_val_batch.numel()
  
file_path: torchmetrics/src/torchmetrics/audio/srmr.py
imports: from typing import Any, Optional, Sequence, Union
  from torch import Tensor, tensor
  from torchmetrics.functional.audio.srmr import _srmr_arg_validate, speech_reverberation_modulation_energy_ratio
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _GAMMATONE_AVAILABLE, _MATPLOTLIB_AVAILABLE, _TORCHAUDIO_AVAILABLE, _TORCHAUDIO_GREATER_EQUAL_0_10
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 2 for calling the SpeechReverberationModulationEnergyRatio obj's underlying member method code with example usage: obj.update(preds) without return types