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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.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, discontinuity, coloration and loudness in that order
.. hint::
Using this metric requires you to have ``librosa`` and ``requests`` installed. Install as
``pip install librosa requests``.
.. caution::
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`."
)
if not isinstance(fs, int) or fs <= 0:
raise ValueError(f"Argument `fs` expected to be a positive integer, but got {fs}")
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)
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