a  d@sddlmZmZmZmZddlmZmZddlm Z ddl m Z ddl m Z mZddlmZmZddgiZe std gZGd dde Zd S) )AnyOptionalSequenceUnion)Tensortensor)$perceptual_evaluation_speech_quality)Metric)_MATPLOTLIB_AVAILABLE_PESQ_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE!PerceptualEvaluationSpeechQualityZpesq&PerceptualEvaluationSpeechQuality.plotcseZdZUdZeed<eed<dZeed<dZeed<dZ eed<d Z e ed <d Z e ed <de ee eddfdd ZeeddddZedddZdeeeedfeeedddZZS)ra Calculate `Perceptual Evaluation of Speech Quality`_ (PESQ). It's a recognized industry standard for audio quality that takes into considerations characteristics such as: audio sharpness, call volume, background noise, clipping, audio interference ect. PESQ returns a score between -0.5 and 4.5 with the higher scores indicating a better quality. This metric is a wrapper for the `pesq package`_. Note that input will be moved to ``cpu`` to perform the metric calculation. 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 - ``pesq`` (:class:`~torch.Tensor`): float tensor with shape ``(...,)`` of PESQ value per sample .. note:: using this metrics requires you to have ``pesq`` install. Either install as ``pip install torchmetrics[audio]`` or ``pip install pesq``. ``pesq`` will compile with your currently installed version of numpy, meaning that if you upgrade numpy at some point in the future you will most likely have to reinstall ``pesq``. Args: fs: sampling frequency, should be 16000 or 8000 (Hz) mode: ``'wb'`` (wide-band) or ``'nb'`` (narrow-band) keep_same_device: whether to move the pesq value to the device of preds n_processes: integer specifiying the number of processes to run in parallel for the metric calculation. Only applies to batches of data and if ``multiprocessing`` package is installed. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ModuleNotFoundError: If ``pesq`` package is not installed ValueError: If ``fs`` is not either ``8000`` or ``16000`` ValueError: If ``mode`` is not either ``"wb"`` or ``"nb"`` Example: >>> import torch >>> from torchmetrics.audio import PerceptualEvaluationSpeechQuality >>> g = torch.manual_seed(1) >>> preds = torch.randn(8000) >>> target = torch.randn(8000) >>> nb_pesq = PerceptualEvaluationSpeechQuality(8000, 'nb') >>> nb_pesq(preds, target) tensor(2.2076) >>> wb_pesq = PerceptualEvaluationSpeechQuality(16000, 'wb') >>> wb_pesq(preds, target) tensor(1.7359) sum_pesqtotalFfull_state_updateis_differentiableThigher_is_bettergplot_lower_boundg@plot_upper_boundN)fsmode n_processeskwargsreturnc stjfi|tstd|dvr4td|||_|dvrPtd|||_t|tsv|dkrvtd|||_ |j dt d d d |j d t dd d dS) NzPerceptualEvaluationSpeechQuality metric requires that `pesq` is installed. Either install as `pip install torchmetrics[audio]` or `pip install pesq`.)i@i>z:Expected argument `fs` to either be 8000 or 16000 but got )wbnbz;Expected argument `mode` to either be 'wb' or 'nb' but got rzCExpected argument `n_processes` to be an int larger than 0 but got rgsum)defaultZdist_reduce_fxr) super__init__r ModuleNotFoundError ValueErrorrr isinstanceintrZ add_stater)selfrrrr __class__`/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/audio/pesq.pyr"\s z*PerceptualEvaluationSpeechQuality.__init__)predstargetrcCsJt|||j|jd|j|jj}|j|7_|j| 7_dS)z*Update state with predictions and targets.FN) rrrrtorZdevicerrZnumel)r'r,r-Z pesq_batchr*r*r+updatevsz(PerceptualEvaluationSpeechQuality.update)rcCs |j|jS)zCompute metric.)rr)r'r*r*r+computesz)PerceptualEvaluationSpeechQuality.compute)valaxrcCs |||S)abPlot 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 PerceptualEvaluationSpeechQuality >>> metric = PerceptualEvaluationSpeechQuality(8000, 'nb') >>> metric.update(torch.rand(8000), torch.rand(8000)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.audio import PerceptualEvaluationSpeechQuality >>> metric = PerceptualEvaluationSpeechQuality(8000, 'nb') >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.rand(8000), torch.rand(8000))) >>> fig_, ax_ = metric.plot(values) )Z_plot)r'r1r2r*r*r+plots&r)r)NN)__name__ __module__ __qualname____doc__r__annotations__rboolrrrfloatrr&strrr"r/r0rrrr r r3 __classcell__r*r*r(r+rs& 6      N)typingrrrrZtorchrrZ"torchmetrics.functional.audio.pesqrZtorchmetrics.metricr Ztorchmetrics.utilities.importsr r Ztorchmetrics.utilities.plotr r Z__doctest_requires__Z__doctest_skip__rr*r*r*r+s