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Perceptual Evaluation of Speech Quality (PESQ)

Module Interface

class torchmetrics.audio.pesq.PerceptualEvaluationSpeechQuality(fs, mode, n_processes=1, **kwargs)[source]

Calculates 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 (Tensor): float tensor with shape (...,time)

  • target (Tensor): float tensor with shape (...,time)

As output of forward and compute the metric returns the following output

  • pesq (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.

Parameters
  • fs (int) – sampling frequency, should be 16000 or 8000 (Hz)

  • mode (str) – 'wb' (wide-band) or 'nb' (narrow-band)

  • keep_same_device – whether to move the pesq value to the device of preds

  • n_processes (int) – 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 (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Raises

Example

>>> from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality
>>> import torch
>>> 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)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface

torchmetrics.functional.audio.pesq.perceptual_evaluation_speech_quality(preds, target, fs, mode, keep_same_device=False, n_processes=1)[source]

Calculates 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.

Note

using 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.

Parameters
  • preds (Tensor) – float tensor with shape (...,time)

  • target (Tensor) – float tensor with shape (...,time)

  • fs (int) – sampling frequency, should be 16000 or 8000 (Hz)

  • mode (str) – 'wb' (wide-band) or 'nb' (narrow-band)

  • keep_same_device (bool) – whether to move the pesq value to the device of preds

  • n_processes (int) – 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.

Return type

Tensor

Returns

Float tensor with shape (...,) of PESQ values per sample

Raises

Example

>>> from torchmetrics.functional.audio.pesq import perceptual_evaluation_speech_quality
>>> import torch
>>> g = torch.manual_seed(1)
>>> preds = torch.randn(8000)
>>> target = torch.randn(8000)
>>> perceptual_evaluation_speech_quality(preds, target, 8000, 'nb')
tensor(2.2076)
>>> perceptual_evaluation_speech_quality(preds, target, 16000, 'wb')
tensor(1.7359)
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