Update audio docs (#1360)

* exclude update and compute * update modular documentation * matching function docs * Apply suggestions from code review Co-authored-by: Jirka <jirka.borovec@seznam.cz> Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com> Co-authored-by: Ethan Harris <ethanwharris@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Lightning-AI · Nov 30, 2022 · 8bcfb73 · 8bcfb73
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diff --git a/docs/source/audio/perceptual_evaluation_speech_quality.rst b/docs/source/audio/perceptual_evaluation_speech_quality.rst
@@ -3,6 +3,8 @@
    :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
    :tags: Audio
 
+.. include:: ../links.rst
+
 ##############################################
 Perceptual Evaluation of Speech Quality (PESQ)
 ##############################################
@@ -11,8 +13,11 @@ Module Interface
 ________________
 
 .. autoclass:: torchmetrics.audio.pesq.PerceptualEvaluationSpeechQuality
+    :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________
 
 .. autofunction:: torchmetrics.functional.audio.pesq.perceptual_evaluation_speech_quality
+    :noindex:
diff --git a/docs/source/audio/permutation_invariant_training.rst b/docs/source/audio/permutation_invariant_training.rst
@@ -14,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.PermutationInvariantTraining
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

diff --git a/docs/source/audio/scale_invariant_signal_distortion_ratio.rst b/docs/source/audio/scale_invariant_signal_distortion_ratio.rst
@@ -3,6 +3,8 @@
    :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
    :tags: Audio
 
+.. include:: ../links.rst
+
 ###################################################
 Scale-Invariant Signal-to-Distortion Ratio (SI-SDR)
 ###################################################
@@ -12,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.ScaleInvariantSignalDistortionRatio
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

diff --git a/docs/source/audio/scale_invariant_signal_noise_ratio.rst b/docs/source/audio/scale_invariant_signal_noise_ratio.rst
@@ -3,6 +3,8 @@
    :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
    :tags: Audio
 
+.. include:: ../links.rst
+
 ##############################################
 Scale-Invariant Signal-to-Noise Ratio (SI-SNR)
 ##############################################
@@ -12,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.ScaleInvariantSignalNoiseRatio
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

diff --git a/docs/source/audio/short_time_objective_intelligibility.rst b/docs/source/audio/short_time_objective_intelligibility.rst
@@ -3,6 +3,8 @@
    :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
    :tags: Audio
 
+.. include:: ../links.rst
+
 ###########################################
 Short-Time Objective Intelligibility (STOI)
 ###########################################
@@ -12,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.audio.stoi.ShortTimeObjectiveIntelligibility
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

diff --git a/docs/source/audio/signal_distortion_ratio.rst b/docs/source/audio/signal_distortion_ratio.rst
@@ -3,6 +3,8 @@
    :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
    :tags: Audio
 
+.. include:: ../links.rst
+
 ################################
 Signal to Distortion Ratio (SDR)
 ################################
@@ -12,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.SignalDistortionRatio
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

diff --git a/docs/source/audio/signal_noise_ratio.rst b/docs/source/audio/signal_noise_ratio.rst
@@ -14,6 +14,7 @@ ________________
 
 .. autoclass:: torchmetrics.SignalNoiseRatio
     :noindex:
+    :exclude-members: update, compute
 
 Functional Interface
 ____________________

diff --git a/docs/source/links.rst b/docs/source/links.rst
@@ -102,3 +102,16 @@
 .. _CLIP score: https://arxiv.org/pdf/2104.08718.pdf
 .. _Huggingface OpenAI: https://huggingface.co/openai
 .. _Theil's U: https://en.wikipedia.org/wiki/Uncertainty_coefficient
+.. _Perceptual Evaluation of Speech Quality: https://en.wikipedia.org/wiki/Perceptual_Evaluation_of_Speech_Quality
+.. _pesq package: https://github.com/ludlows/python-pesq
+.. _Cees Taal's website: http://www.ceestaal.nl/code/
+.. _pystoi package: https://github.com/mpariente/pystoi
+.. _stoi ref1: https://ieeexplore.ieee.org/document/5495701
+.. _stoi ref2: https://ieeexplore.ieee.org/document/5713237
+.. _stoi ref3: https://ieeexplore.ieee.org/document/7539284
+.. _sdr ref1: https://ieeexplore.ieee.org/document/1643671
+.. _sdr ref2: https://arxiv.org/abs/2110.06440
+.. _Scale-invariant signal-to-distortion ratio: https://arxiv.org/abs/1811.02508
+.. _Scale-invariant signal-to-noise ratio: https://arxiv.org/abs/1711.00541
+.. _Signal-to-noise ratio: https://arxiv.org/abs/1811.02508
+.. _Permutation invariant training: https://arxiv.org/abs/1607.00325
diff --git a/src/torchmetrics/audio/pesq.py b/src/torchmetrics/audio/pesq.py
@@ -23,21 +23,28 @@
 
 
 class PerceptualEvaluationSpeechQuality(Metric):
-    """Perceptual Evaluation of Speech Quality (PESQ)
+    """Calculates `Perceptual Evaluation of Speech Quality`_ (PESQ). It's a recognized industry standard for audio
+    quality that takes into considerations characteristics such as: audio sharpness, call volume, background noise,
+    clipping, audio interference ect. PESQ returns a score between -0.5 and 4.5 with the higher scores indicating a
+    better quality.
 
-    This is a wrapper for the pesq package [1]. Note that input will be moved to `cpu`
-    to perform the metric calculation.
+    This metric is a wrapper for the `pesq package`_. Note that input will be moved to ``cpu`` to perform the metric
+    calculation.
+
+    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
+
+    - ``pesq`` (: :class:`~torch.Tensor`): float tensor with shape ``(...,)`` of PESQ value per sample
 
     .. note:: using this metrics requires you to have ``pesq`` install. Either install as ``pip install
-        torchmetrics[audio]`` or ``pip install pesq``. Note that ``pesq`` will compile with your currently
+        torchmetrics[audio]`` or ``pip install pesq``. ``pesq`` will compile with your currently
         installed version of numpy, meaning that if you upgrade numpy at some point in the future you will
         most likely have to reinstall ``pesq``.
 
-    Forward accepts
-
-    - ``preds``: ``shape [...,time]``
-    - ``target``: ``shape [...,time]``
-
     Args:
         fs: sampling frequency, should be 16000 or 8000 (Hz)
         mode: ``'wb'`` (wide-band) or ``'nb'`` (narrow-band)
@@ -66,9 +73,6 @@ class PerceptualEvaluationSpeechQuality(Metric):
         >>> wb_pesq = PerceptualEvaluationSpeechQuality(16000, 'wb')
         >>> wb_pesq(preds, target)
         tensor(1.7359)
-
-    References:
-        [1] https://github.com/ludlows/python-pesq
     """
 
     sum_pesq: Tensor
@@ -104,12 +108,7 @@ def __init__(
         self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:
-        """Update state with predictions and targets.
-
-        Args:
-            preds: Predictions from model
-            target: Ground truth values
-        """
+        """Update state with predictions and targets."""
         pesq_batch = perceptual_evaluation_speech_quality(
             preds, target, self.fs, self.mode, False, self.n_processes
         ).to(self.sum_pesq.device)
@@ -118,5 +117,5 @@ def update(self, preds: Tensor, target: Tensor) -> None:
         self.total += pesq_batch.numel()
 
     def compute(self) -> Tensor:
-        """Computes average PESQ."""
+        """Computes metric."""
         return self.sum_pesq / self.total
diff --git a/src/torchmetrics/audio/pit.py b/src/torchmetrics/audio/pit.py
@@ -14,36 +14,36 @@
 from typing import Any, Callable, Dict
 
 from torch import Tensor, tensor
+from typing_extensions import Literal
 
 from torchmetrics.functional.audio.pit import permutation_invariant_training
 from torchmetrics.metric import Metric
 
 
 class PermutationInvariantTraining(Metric):
-    """Permutation invariant training (PermutationInvariantTraining). The PermutationInvariantTraining implements
-    the famous Permutation Invariant Training method.
+    """Calculates `Permutation invariant training`_ (PIT) that can evaluate models for speaker independent multi-
+    talker speech separation in a permutation invariant way.
 
-    [1] in speech separation field in order to calculate audio metrics in a permutation invariant way.
+    As input to ``forward`` and ``update`` the metric accepts the following input
 
-    Forward accepts
+    - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(batch_size,num_speakers,...)``
+    - ``target`` (: :class:`~torch.Tensor`): float tensor with shape ``(batch_size,num_speakers,...)``
 
-    - ``preds``: ``shape [batch, spk, ...]``
-    - ``target``: ``shape [batch, spk, ...]``
+    As output of `forward` and `compute` the metric returns the following output
+
+    - ``pesq`` (: :class:`~torch.Tensor`): float scalar tensor with average PESQ value over samples
 
     Args:
         metric_func:
             a metric function accept a batch of target and estimate,
-            i.e. ``metric_func(preds[:, i, ...], target[:, j, ...])``, and returns a batch of metric tensors ``[batch]``
+            i.e. ``metric_func(preds[:, i, ...], target[:, j, ...])``, and returns a batch of metric
+            tensors ``(batch,)``
         eval_func:
             the function to find the best permutation, can be 'min' or 'max', i.e. the smaller the better
             or the larger the better.
-
         kwargs: Additional keyword arguments for either the ``metric_func`` or distributed communication,
             see :ref:`Metric kwargs` for more info.
 
-    Returns:
-        average PermutationInvariantTraining metric
-
     Example:
         >>> import torch
         >>> from torchmetrics import PermutationInvariantTraining
@@ -54,11 +54,6 @@ class PermutationInvariantTraining(Metric):
         >>> pit = PermutationInvariantTraining(scale_invariant_signal_noise_ratio, 'max')
         >>> pit(preds, target)
         tensor(-2.1065)
-
-    Reference:
-        [1]	D. Yu, M. Kolbaek, Z.-H. Tan, J. Jensen, Permutation invariant training of deep models for
-        speaker-independent multi-talker speech separation, in: 2017 IEEE Int. Conf. Acoust. Speech
-        Signal Process. ICASSP, IEEE, New Orleans, LA, 2017: pp. 241–245. https://doi.org/10.1109/ICASSP.2017.7952154.
     """
 
     full_state_update: bool = False
@@ -69,7 +64,7 @@ class PermutationInvariantTraining(Metric):
     def __init__(
         self,
         metric_func: Callable,
-        eval_func: str = "max",
+        eval_func: Literal["max", "min"] = "max",
         **kwargs: Any,
     ) -> None:
         base_kwargs: Dict[str, Any] = {
@@ -86,17 +81,12 @@ def __init__(
         self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:
-        """Update state with predictions and targets.
-
-        Args:
-            preds: Predictions from model
-            target: Ground truth values
-        """
+        """Update state with predictions and targets."""
         pit_metric = permutation_invariant_training(preds, target, self.metric_func, self.eval_func, **self.kwargs)[0]
 
         self.sum_pit_metric += pit_metric.sum()
         self.total += pit_metric.numel()
 
     def compute(self) -> Tensor:
-        """Computes average PermutationInvariantTraining metric."""
+        """Computes metric."""
         return self.sum_pit_metric / self.total