# 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.snr import ( complex_scale_invariant_signal_noise_ratio, scale_invariant_signal_noise_ratio, signal_noise_ratio, ) from torchmetrics.metric import Metric from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = [ "SignalNoiseRatio.plot", "ScaleInvariantSignalNoiseRatio.plot", "ComplexScaleInvariantSignalNoiseRatio.plot", ] class SignalNoiseRatio(Metric): r"""Calculate `Signal-to-noise ratio`_ (SNR_) meric for evaluating quality of audio. .. math:: \text{SNR} = \frac{P_{signal}}{P_{noise}} where :math:`P` denotes the power of each signal. The SNR metric compares the level of the desired signal to the level of background noise. Therefore, a high value of SNR means that the audio is clear. 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 - ``snr`` (:class:`~torch.Tensor`): float scalar tensor with average SNR value over samples Args: zero_mean: if to zero mean target and preds or not kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: TypeError: if target and preds have a different shape Example: >>> from torch import tensor >>> from torchmetrics.audio import SignalNoiseRatio >>> target = tensor([3.0, -0.5, 2.0, 7.0]) >>> preds = tensor([2.5, 0.0, 2.0, 8.0]) >>> snr = SignalNoiseRatio() >>> snr(preds, target) tensor(16.1805) """ full_state_update: bool = False is_differentiable: bool = True higher_is_better: bool = True sum_snr: Tensor total: Tensor plot_lower_bound: Optional[float] = None plot_upper_bound: Optional[float] = None def __init__( self, zero_mean: bool = False, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.zero_mean = zero_mean self.add_state("sum_snr", 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.""" snr_batch = signal_noise_ratio(preds=preds, target=target, zero_mean=self.zero_mean) self.sum_snr += snr_batch.sum() self.total += snr_batch.numel() def compute(self) -> Tensor: """Compute metric.""" return self.sum_snr / self.total def plot( self, val: Optional[Union[Tensor, Sequence[Tensor]]] = 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 SignalNoiseRatio >>> metric = SignalNoiseRatio() >>> metric.update(torch.rand(4), torch.rand(4)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.audio import SignalNoiseRatio >>> metric = SignalNoiseRatio() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.rand(4), torch.rand(4))) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax) class ScaleInvariantSignalNoiseRatio(Metric): """Calculate `Scale-invariant signal-to-noise ratio`_ (SI-SNR) metric for evaluating quality of audio. 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 - ``si_snr`` (:class:`~torch.Tensor`): float scalar tensor with average SI-SNR value over samples Args: kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: TypeError: if target and preds have a different shape Example: >>> import torch >>> from torch import tensor >>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio >>> target = tensor([3.0, -0.5, 2.0, 7.0]) >>> preds = tensor([2.5, 0.0, 2.0, 8.0]) >>> si_snr = ScaleInvariantSignalNoiseRatio() >>> si_snr(preds, target) tensor(15.0918) """ is_differentiable = True sum_si_snr: Tensor total: Tensor higher_is_better = True plot_lower_bound: Optional[float] = None plot_upper_bound: Optional[float] = None def __init__( self, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.add_state("sum_si_snr", 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.""" si_snr_batch = scale_invariant_signal_noise_ratio(preds=preds, target=target) self.sum_si_snr += si_snr_batch.sum() self.total += si_snr_batch.numel() def compute(self) -> Tensor: """Compute metric.""" return self.sum_si_snr / 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 ScaleInvariantSignalNoiseRatio >>> metric = ScaleInvariantSignalNoiseRatio() >>> metric.update(torch.rand(4), torch.rand(4)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio >>> metric = ScaleInvariantSignalNoiseRatio() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.rand(4), torch.rand(4))) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax) class ComplexScaleInvariantSignalNoiseRatio(Metric): """Calculate `Complex scale-invariant signal-to-noise ratio`_ (C-SI-SNR) metric for evaluating quality of audio. As input to `forward` and `update` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): real float tensor with shape ``(...,frequency,time,2)`` or complex float tensor with shape ``(..., frequency,time)`` - ``target`` (:class:`~torch.Tensor`): real float tensor with shape ``(...,frequency,time,2)`` or complex float tensor with shape ``(..., frequency,time)`` As output of `forward` and `compute` the metric returns the following output - ``c_si_snr`` (:class:`~torch.Tensor`): float scalar tensor with average C-SI-SNR value over samples Args: zero_mean: if to zero mean target and preds or not kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``zero_mean`` is not an bool TypeError: If ``preds`` is not the shape (..., frequency, time, 2) (after being converted to real if it is complex). If ``preds`` and ``target`` does not have the same shape. Example: >>> from torch import randn >>> from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio >>> preds = randn((1,257,100,2)) >>> target = randn((1,257,100,2)) >>> c_si_snr = ComplexScaleInvariantSignalNoiseRatio() >>> c_si_snr(preds, target) tensor(-38.8832) """ is_differentiable = True ci_snr_sum: Tensor num: Tensor higher_is_better = True plot_lower_bound: Optional[float] = None plot_upper_bound: Optional[float] = None def __init__( self, zero_mean: bool = False, **kwargs: Any, ) -> None: super().__init__(**kwargs) if not isinstance(zero_mean, bool): raise ValueError(f"Expected argument `zero_mean` to be an bool, but got {zero_mean}") self.zero_mean = zero_mean self.add_state("ci_snr_sum", default=tensor(0.0), dist_reduce_fx="sum") self.add_state("num", default=tensor(0), dist_reduce_fx="sum") def update(self, preds: Tensor, target: Tensor) -> None: """Update state with predictions and targets.""" v = complex_scale_invariant_signal_noise_ratio(preds=preds, target=target, zero_mean=self.zero_mean) self.ci_snr_sum += v.sum() self.num += v.numel() def compute(self) -> Tensor: """Compute metric.""" return self.ci_snr_sum / self.num 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 ComplexScaleInvariantSignalNoiseRatio >>> metric = ComplexScaleInvariantSignalNoiseRatio() >>> metric.update(torch.rand(1,257,100,2), torch.rand(1,257,100,2)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio >>> metric = ComplexScaleInvariantSignalNoiseRatio() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.rand(1,257,100,2), torch.rand(1,257,100,2))) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax)