o yio @sbddlZddlmZddlmZddlmZddededed efd d Zdeded efd d ZdS)N)Tensor'scale_invariant_signal_distortion_ratio)_check_same_shapeFpredstarget zero_meanreturncCst||t|jj}|r"|tj|ddd}|tj|ddd}||}tj|ddd|tj|ddd|}dt|}|S)aCalculates `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. Args: preds: float tensor with shape ``(...,time)`` target: float tensor with shape ``(...,time)`` zero_mean: if to zero mean target and preds or not Returns: Float tensor with shape ``(...,)`` of SNR values per sample Raises: RuntimeError: If ``preds`` and ``target`` does not have the same shape Example: >>> from torchmetrics.functional.audio import signal_noise_ratio >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0]) >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0]) >>> signal_noise_ratio(preds, target) tensor(16.1805) T)dimkeepdim)r )rtorchfinfodtypeepsmeansumlog10)rrrrnoise snr_valuerU/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/audio/snr.pysignal_noise_ratios ,rcCst||ddS)a`Scale-invariant signal-to-noise ratio`_ (SI-SNR). Args: preds: float tensor with shape ``(...,time)`` target: float tensor with shape ``(...,time)`` Returns: Float tensor with shape ``(...,)`` of SI-SNR values per sample Raises: RuntimeError: If ``preds`` and ``target`` does not have the same shape Example: >>> import torch >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0]) >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0]) >>> scale_invariant_signal_noise_ratio(preds, target) tensor(15.0918) T)rrrr)rrrrr"scale_invariant_signal_noise_ratioAsr)F) rr!torchmetrics.functional.audio.sdrrtorchmetrics.utilities.checksrboolrrrrrrs    +