o yi2@s\ddlmZddlmZmZddlmZmZddlm Z Gddde Z Gddde Z d S) )Any)Tensortensor)"scale_invariant_signal_noise_ratiosignal_noise_ratio)MetriccseZdZUdZdZeed<dZeed<dZeed<e ed<e ed< dd ed e d d ffd d Z de de d d fddZ d e fddZ ZS)SignalNoiseRatioa?Calculates `Signal-to-noise ratio`_ (SNR_) meric for evaluating quality of audio. It is defined as: .. 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: >>> import torch >>> from torchmetrics import SignalNoiseRatio >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0]) >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0]) >>> snr = SignalNoiseRatio() >>> snr(preds, target) tensor(16.1805) Ffull_state_updateTis_differentiablehigher_is_bettersum_snrtotal zero_meankwargsreturnNc sDtjdi|||_|jdtddd|jdtddddS)Nr sumdefaultdist_reduce_fxr r)super__init__r add_stater)selfrr __class__rJ/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/audio/snr.pyr?szSignalNoiseRatio.__init__predstargetcCs8t|||jd}|j|7_|j|7_dS)*Update state with predictions and targets.)rrrN)rrr rr numel)rrr snr_batchrrrupdateJszSignalNoiseRatio.updatecC |j|jSzComputes metric.)r r rrrrcomputeQ zSignalNoiseRatio.compute)F)__name__ __module__ __qualname____doc__r bool__annotations__r r rrrr#r' __classcell__rrrrrs"  "   rcsheZdZUdZdZeed<eed<dZdeddffdd Z d ed eddfd d Z defddZ Z S)ScaleInvariantSignalNoiseRatioaCalculates `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 torchmetrics import ScaleInvariantSignalNoiseRatio >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0]) >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0]) >>> si_snr = ScaleInvariantSignalNoiseRatio() >>> si_snr(preds, target) tensor(15.0918) T sum_si_snrr rrNc s>tjdi||jdtddd|jdtddddS)Nr1rrrr rr)rrrr)rrrrrrxsz'ScaleInvariantSignalNoiseRatio.__init__rrcCs4t||d}|j|7_|j|7_dS)r )rrN)rr1rr r!)rrr si_snr_batchrrrr#s z%ScaleInvariantSignalNoiseRatio.updatecCr$r%)r1r r&rrrr'r(z&ScaleInvariantSignalNoiseRatio.compute) r)r*r+r,r rr.r rrr#r'r/rrrrr0Vs  r0N) typingrtorchrr!torchmetrics.functional.audio.snrrrtorchmetrics.metricrrr0rrrrs  @