o .wi@sddlmZddlmZmZmZddlmZmZddl m Z ddl m Z ddl mZmZddlmZmZdd giZes>d gZGd dde Zd S) )Sequence)AnyOptionalUnion)Tensortensor)$short_time_objective_intelligibility)Metric)_MATPLOTLIB_AVAILABLE_PYSTOI_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE!ShortTimeObjectiveIntelligibilitypystoi&ShortTimeObjectiveIntelligibility.plotc seZdZUdZeed<eed<dZeed<dZeed<dZ eed<d Z e ed <d Z e ed < dd e dededdffdd ZdededdfddZdefddZddeeeedfdeedefddZZS) raCalculate STOI (Short-Time Objective Intelligibility) metric for evaluating speech signals. Intelligibility measure which is highly correlated with the intelligibility of degraded speech signals, e.g., due to additive noise, single-/multi-channel noise reduction, binary masking and vocoded speech as in CI simulations. The STOI-measure is intrusive, i.e., a function of the clean and degraded speech signals. STOI may be a good alternative to the speech intelligibility index (SII) or the speech transmission index (STI), when you are interested in the effect of nonlinear processing to noisy speech, e.g., noise reduction, binary masking algorithms, on speech intelligibility. Description taken from `Cees Taal's website`_ and for further details see `STOI ref1`_ and `STOI ref2`_. This metric is a wrapper for the `pystoi package`_. As the implementation backend implementation only supports calculations on CPU, all input will automatically be moved to CPU to perform the metric calculation before being moved back to the original device. 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 - ``stoi`` (:class:`~torch.Tensor`): float scalar tensor .. hint:: Using this metrics requires you to have ``pystoi`` install. Either install as ``pip install torchmetrics[audio]`` or ``pip install pystoi``. Args: fs: sampling frequency (Hz) extended: whether to use the extended STOI described in `STOI ref3`_. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ModuleNotFoundError: If ``pystoi`` package is not installed Example: >>> from torch import randn >>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility >>> preds = randn(8000) >>> target = randn(8000) >>> stoi = ShortTimeObjectiveIntelligibility(8000, False) >>> stoi(preds, target) tensor(-0.084...) sum_stoitotalFfull_state_updateis_differentiableThigher_is_betterplot_lower_boundg?plot_upper_boundfsextendedkwargsreturnNc sVtjdi|tstd||_||_|jdtddd|jdtddddS) Nz}STOI metric requires that `pystoi` is installed. Either install as `pip install torchmetrics[audio]` or `pip install pystoi`.rrsum)defaultdist_reduce_fxrr)super__init__r ModuleNotFoundErrorrr add_stater)selfrrr __class__r T/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/audio/stoi.pyr"Vsz*ShortTimeObjectiveIntelligibility.__init__predstargetcCsFt|||j|jd|jj}|j|7_|j|7_dS)z*Update state with predictions and targets.FN) rrrtordevicerrnumel)r%r)r* stoi_batchr r r(updatehs z(ShortTimeObjectiveIntelligibility.updatecCs |j|jS)zCompute metric.)rr)r%r r r(computeqs z)ShortTimeObjectiveIntelligibility.computevalaxcCs |||S)aPlot 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 >>> from torch import randn >>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility >>> preds = randn(8000) >>> target = randn(8000) >>> metric = ShortTimeObjectiveIntelligibility(8000, False) >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import randn >>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility >>> metric = ShortTimeObjectiveIntelligibility(8000, False) >>> preds = randn(8000) >>> target = randn(8000) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r%r1r2r r r(plotus *r)F)NN)__name__ __module__ __qualname____doc__r__annotations__rboolrrrfloatrintrr"r/r0rrrr r r4 __classcell__r r r&r(rs, /      2N)collections.abcrtypingrrrtorchrr"torchmetrics.functional.audio.stoirtorchmetrics.metricr torchmetrics.utilities.importsr r torchmetrics.utilities.plotr r __doctest_requires____doctest_skip__rr r r r(s