:j|ddlmZddlZddlZddlmZddlmZddlm Z m Z ddgiZ dded ed e d e d ed e defdZy))AnyN)Tensor)_check_same_shape)_MULTIPROCESSING_AVAILABLE_PESQ_AVAILABLE)$perceptual_evaluation_speech_qualitypesqpredstargetfsmodekeep_same_device n_processesreturnc 6ts tdddl}dtdtfd}t j |}|dvrtd||d vrtd |t|||jd k(r|j||jjj|jjj|} tj| } n|jd |j d jjj} |jd |j d jjj} t"r1|d k7r,|j%|| | || } t j&| } net j(| j d} t+| j dD]'} |j|| | ddf| | ddf|| | <)tj,| || j/t j0} | jt3| } |r| j5|j6S| S)aCalculate `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 etc. 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. .. hint:: Usingsing this metrics requires you to have ``pesq`` install. Either install as ``pip install torchmetrics[audio]`` or ``pip install pesq``. Note that ``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: preds: float tensor with shape ``(...,time)`` target: float tensor with shape ``(...,time)`` 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 specifying the number of processes to run in parallel for the metric calculation. Only applies to batches of data and if ``multiprocessing`` package is installed. Returns: Float tensor with shape ``(...,)`` of PESQ values per sample 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"`` RuntimeError: If ``preds`` and ``target`` do not have the same shape Example: >>> from torch import randn >>> from torchmetrics.functional.audio.pesq import perceptual_evaluation_speech_quality >>> preds = randn(8000) >>> target = randn(8000) >>> perceptual_evaluation_speech_quality(preds, target, 8000, 'nb') tensor(2.2885) >>> perceptual_evaluation_speech_quality(preds, target, 16000, 'wb') tensor(1.6805) zwPESQ metric requires that pesq is installed. Either install as `pip install torchmetrics[audio]` or `pip install pesq`.rNxrc\tjt|tjS)N)np issubdtypetypenumber)rs w/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/audio/pesq.py_issubtype_numberz?perceptual_evaluation_speech_quality.._issubtype_numberYs}}T!Wbii00)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 ) n_processor)shape)rModuleNotFoundErrorr rboolr vectorize ValueErrorrndimdetachcpunumpytorchtensorreshaper r pesq_batcharrayemptyrange from_numpyastypefloat32lentodevice)r r r r rr pesq_backendr_filter_error_msg pesq_val_nppesq_valpreds_np target_npbs rrrsCp ! Z   1S1T1 %67 UVXUYZ[[ <VW[V\]^^eV$ zzQ"''FMMO,?,?,A,G,G,I5<<>K]K]K_KeKeKgimn << ,==U[[_5<<>BBDJJLNN2u{{27>>@DDFLLN %+*:&11"i4]h1iK((;/K(((..*;=K8>>!,- ^!-!2!22yAQRTUQUX\!] A ^##K0A+0N$O$V$VWYWaWa$bc##CM2{{5<<(( Or)Fr)typingrr(rr)rtorchmetrics.utilities.checksrtorchmetrics.utilities.importsrr__doctest_requires__intstrr"rrrrDs} ;VAF8L# ^ ^ ^ ^  ^  ^  ^ ^r