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# 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)
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