# 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 collections.abc import Sequence from typing import Any, Optional, Union from torch import Tensor, tensor from torchmetrics.functional.audio.pesq import perceptual_evaluation_speech_quality from torchmetrics.metric import Metric from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _PESQ_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE __doctest_requires__ = {"PerceptualEvaluationSpeechQuality": ["pesq"]} if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["PerceptualEvaluationSpeechQuality.plot"] class PerceptualEvaluationSpeechQuality(Metric): """Calculate `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 etc. PESQ returns a score between -0.5 and 4.5 with the higher scores indicating a better quality. 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 of PESQ value reduced across the batch .. hint:: Using this metrics requires you to have ``pesq`` install. Either install as ``pip install 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``. .. caution:: The ``forward`` and ``compute`` methods in this class return a single (reduced) PESQ value for a batch. To obtain a PESQ value for each sample, you may use the functional counterpart in :func:`~torchmetrics.functional.audio.pesq.perceptual_evaluation_speech_quality`. Args: fs: sampling frequency, should be 16000 or 8000 (Hz) mode: ``'wb'`` (wide-band) or ``'nb'`` (narrow-band) keep_same_device: whether to move the pesq value to the device of preds n_processes: integer specifying the number of processes to run in parallel for the metric calculation. Only applies to batches of data and if ``multiprocessing`` package is installed. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ModuleNotFoundError: If ``pesq`` package is not installed ValueError: If ``fs`` is not either ``8000`` or ``16000`` ValueError: If ``mode`` is not either ``"wb"`` or ``"nb"`` Example: >>> from torch import randn >>> from torchmetrics.audio import PerceptualEvaluationSpeechQuality >>> preds = randn(8000) >>> target = randn(8000) >>> pesq = PerceptualEvaluationSpeechQuality(8000, 'nb') >>> pesq(preds, target) tensor(2.2885) >>> wb_pesq = PerceptualEvaluationSpeechQuality(16000, 'wb') >>> wb_pesq(preds, target) tensor(1.6805) """ sum_pesq: Tensor total: Tensor full_state_update: bool = False is_differentiable: bool = False higher_is_better: bool = True plot_lower_bound: float = -0.5 plot_upper_bound: float = 4.5 def __init__( self, fs: int, mode: str, n_processes: int = 1, **kwargs: Any, ) -> None: super().__init__(**kwargs) if not _PESQ_AVAILABLE: raise ModuleNotFoundError( "PerceptualEvaluationSpeechQuality metric requires that `pesq` is installed." " Either install as `pip install torchmetrics[audio]` or `pip install pesq`." ) if fs not in (8000, 16000): raise ValueError(f"Expected argument `fs` to either be 8000 or 16000 but got {fs}") self.fs = fs if mode not in ("wb", "nb"): raise ValueError(f"Expected argument `mode` to either be 'wb' or 'nb' but got {mode}") self.mode = mode if not isinstance(n_processes, int) and n_processes <= 0: raise ValueError(f"Expected argument `n_processes` to be an int larger than 0 but got {n_processes}") self.n_processes = n_processes self.add_state("sum_pesq", default=tensor(0.0), dist_reduce_fx="sum") 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.""" pesq_batch = perceptual_evaluation_speech_quality( preds, target, self.fs, self.mode, False, self.n_processes ).to(self.sum_pesq.device) self.sum_pesq += pesq_batch.sum() self.total += pesq_batch.numel() def compute(self) -> Tensor: """Compute metric.""" return self.sum_pesq / 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: An 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 PerceptualEvaluationSpeechQuality >>> metric = PerceptualEvaluationSpeechQuality(8000, 'nb') >>> metric.update(torch.rand(8000), torch.rand(8000)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.audio import PerceptualEvaluationSpeechQuality >>> metric = PerceptualEvaluationSpeechQuality(8000, 'nb') >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.rand(8000), torch.rand(8000))) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax)