Add NISQA metric

CHANGELOG.md

@@ -12,7 +12,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
--
+- Added a new audio metric `NISQA` ([#2792](https://github.com/PyTorchLightning/metrics/pull/2792))
 
 
 ### Changed

docs/source/audio/non_intrusive_speech_quality_assessment.rst

@@ -0,0 +1,19 @@
+.. customcarditem::
+   :header: Non-Intrusive Speech Quality Assessment (NISQA v2.0)
+   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
+   :tags: Audio
+
+####################################################
+Non-Intrusive Speech Quality Assessment (NISQA v2.0)
+####################################################
+
+Module Interface
+________________
+
+.. autoclass:: torchmetrics.audio.nisqa.NonIntrusiveSpeechQualityAssessment
+    :exclude-members: update, compute
+
+Functional Interface
+____________________
+
+.. autofunction:: torchmetrics.functional.audio.nisqa.non_intrusive_speech_quality_assessment

src/torchmetrics/audio/__init__.py

@@ -28,6 +28,7 @@
     _ONNXRUNTIME_AVAILABLE,
     _PESQ_AVAILABLE,
     _PYSTOI_AVAILABLE,
+    _REQUESTS_AVAILABLE,
     _SCIPI_AVAILABLE,
     _TORCHAUDIO_AVAILABLE,
 )
@@ -68,3 +69,8 @@
     from torchmetrics.audio.dnsmos import DeepNoiseSuppressionMeanOpinionScore
 
     __all__ += ["DeepNoiseSuppressionMeanOpinionScore"]
+
+if _LIBROSA_AVAILABLE and _REQUESTS_AVAILABLE:
+    from torchmetrics.audio.nisqa import NonIntrusiveSpeechQualityAssessment
+
+    __all__ += ["NonIntrusiveSpeechQualityAssessment"]

src/torchmetrics/audio/nisqa.py

@@ -0,0 +1,150 @@
+# Copyright The Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Optional, Sequence, Union
+
+from torch import Tensor, tensor
+
+from torchmetrics.functional.audio.nisqa import non_intrusive_speech_quality_assessment
+from torchmetrics.metric import Metric
+from torchmetrics.utilities.imports import (
+    _LIBROSA_AVAILABLE,
+    _MATPLOTLIB_AVAILABLE,
+    _REQUESTS_AVAILABLE,
+)
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+__doctest_requires__ = {"NonIntrusiveSpeechQualityAssessment": ["librosa", "requests"]}
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["NonIntrusiveSpeechQualityAssessment.plot"]
+
+
+class NonIntrusiveSpeechQualityAssessment(Metric):
+    """Non-Intrusive Speech Quality Assessment (NISQA v2.0) [1], [2].
+
+    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
+
+    - ``nisqa`` (:class:`~torch.Tensor`): float tensor reduced across the batch with shape ``(5,)`` corresponding to
+      overall MOS, noisiness, coloration, discontinuity and loudness.
+
+    .. note:: Using this metric requires you to have ``librosa`` and ``requests`` installed. Install as
+        ``pip install librosa requests``.
+
+    .. note:: The ``forward`` and ``compute`` methods in this class return values reduced across the batch. To obtain
+        values for each sample, you may use the functional counterpart
+        :func:`~torchmetrics.functional.audio.nisqa.non_intrusive_speech_quality_assessment`.
+
+    Args:
+        fs: sampling frequency of input
+
+    Raises:
+        ModuleNotFoundError:
+            If ``librosa`` or ``requests`` are not installed
+
+    Example:
+        >>> import torch
+        >>> from torchmetrics.audio import NonIntrusiveSpeechQualityAssessment
+        >>> _ = torch.manual_seed(42)
+        >>> preds = torch.randn(16000)
+        >>> nisqa = NonIntrusiveSpeechQualityAssessment(16000)
+        >>> nisqa(preds)
+        tensor([1.0433, 1.9545, 2.6087, 1.3460, 1.7117])
+
+    References:
+        - [1] G. Mittag and S. Möller, "Non-intrusive speech quality assessment for super-wideband speech communication
+          networks", in Proc. ICASSP, 2019.
+        - [2] G. Mittag, B. Naderi, A. Chehadi and S. Möller, "NISQA: A deep CNN-self-attention model for
+          multidimensional speech quality prediction with crowdsourced datasets", in Proc. INTERSPEECH, 2021.
+
+    """
+
+    sum_nisqa: Tensor
+    total: Tensor
+    full_state_update: bool = False
+    is_differentiable: bool = False
+    higher_is_better: bool = True
+    plot_lower_bound: float = 0.0
+    plot_upper_bound: float = 5.0
+
+    def __init__(self, fs: int, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        if not _LIBROSA_AVAILABLE or not _REQUESTS_AVAILABLE:
+            raise ModuleNotFoundError(
+                "NISQA metric requires that librosa and requests are installed. "
+                "Install as `pip install librosa requests`."
+            )
+        self.fs = fs
+
+        self.add_state("sum_nisqa", default=tensor([0.0, 0.0, 0.0, 0.0, 0.0]), dist_reduce_fx="sum")
+        self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
+
+    def update(self, preds: Tensor) -> None:
+        """Update state with predictions."""
+        nisqa_batch = non_intrusive_speech_quality_assessment(
+            preds,
+            self.fs,
+        ).to(self.sum_nisqa.device)
+
+        nisqa_batch = nisqa_batch.reshape(-1, 5)
+        self.sum_nisqa += nisqa_batch.sum(dim=0)
+        self.total += nisqa_batch.shape[0]
+
+    def compute(self) -> Tensor:
+        """Compute metric."""
+        return self.sum_nisqa / self.total
+
+    def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_AX_TYPE] = None) -> _PLOT_OUT_TYPE:
+        """Plot a single or multiple values from the metric.
+
+        Args:
+            val: 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: A matplotlib axis object. If provided will add plot to that axis
+
+        Returns:
+            Figure and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If ``matplotlib`` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> import torch
+            >>> from torchmetrics.audio import NonIntrusiveSpeechQualityAssessment
+            >>> metric = NonIntrusiveSpeechQualityAssessment(16000)
+            >>> metric.update(torch.randn(16000))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics.audio import NonIntrusiveSpeechQualityAssessment
+            >>> metric = NonIntrusiveSpeechQualityAssessment(16000)
+            >>> values = []
+            >>> for _ in range(10):
+            ...     values.append(metric(torch.randn(16000)))
+            >>> fig_, ax_ = metric.plot(values)
+
+        """
+        return self._plot(val, ax)

src/torchmetrics/functional/audio/__init__.py

@@ -28,6 +28,7 @@
     _ONNXRUNTIME_AVAILABLE,
     _PESQ_AVAILABLE,
     _PYSTOI_AVAILABLE,
+    _REQUESTS_AVAILABLE,
     _SCIPI_AVAILABLE,
     _TORCHAUDIO_AVAILABLE,
 )
@@ -69,3 +70,8 @@
     from torchmetrics.functional.audio.dnsmos import deep_noise_suppression_mean_opinion_score
 
     __all__ += ["deep_noise_suppression_mean_opinion_score"]
+
+if _LIBROSA_AVAILABLE and _REQUESTS_AVAILABLE:
+    from torchmetrics.functional.audio.nisqa import non_intrusive_speech_quality_assessment
+
+    __all__ += ["non_intrusive_speech_quality_assessment"]