.. _sphx_glr_gallery_audio:

Audio domain
============

Audio-domain metrics are essential for assessing the performance of models in tasks such as speech recognition, audio classification, and sound event detection. TorchMetrics offers a comprehensive set of specialized metrics tailored for these audio-specific purposes. Utilizing these metrics from TorchMetrics aids in the development of more accurate and resilient audio-based models, ensuring that performance evaluations are both meaningful and directly applicable to real-world audio tasks.



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    <div class="sphx-glr-thumbnails">

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    <div class="sphx-glr-thumbcontainer" tooltip="This notebook will guide you through calculating the Perceptual Evaluation of Speech Quality (PESQ) score,  a key metric in assessing how effective noise reduction and enhancement techniques are in improving speech quality.  PESQ is widely adopted in industries such as telecommunications, VoIP, and audio processing.  It provides an objective way to measure the perceived quality of speech signals from a human listener&#x27;s perspective.">

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  .. image:: /gallery/audio/images/thumb/sphx_glr_pesq_thumb.png
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  :ref:`sphx_glr_gallery_audio_pesq.py`

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      <div class="sphx-glr-thumbnail-title">Evaluating Speech Quality with PESQ metric</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Imagine developing a song recognition application. The software&#x27;s goal is to recognize a song even when it&#x27;s played in a noisy environment, similar to Shazam. To achieve this, you want to enhance the audio quality by reducing the noise and evaluating the improvement using the Signal-to-Noise Ratio (SNR).">

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  .. image:: /gallery/audio/images/thumb/sphx_glr_signal_to_noise_ratio_thumb.png
    :alt:

  :ref:`sphx_glr_gallery_audio_signal_to_noise_ratio.py`

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      <div class="sphx-glr-thumbnail-title">Signal-to-Noise Ratio</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Consider a use case where we want to find the highest-quality speaker signal based on an example target voice. Using a text-to-speech model, we generate speech for five different synthetic speakers, each with unique speaker embeddings. We then compare each generated voice to a reference speaker using Perceptual Evaluation of Speech Quality (PESQ), a metric that assesses how closely the generated audio matches the target.">

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  .. image:: /gallery/audio/images/thumb/sphx_glr_text_to_speech_thumb.png
    :alt:

  :ref:`sphx_glr_gallery_audio_text_to_speech.py`

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      <div class="sphx-glr-thumbnail-title">Perceptual Evaluation of Text-to-Speech with PESQ</div>
    </div>


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    </div>


.. toctree::
   :hidden:

   /gallery/audio/pesq
   /gallery/audio/signal_to_noise_ratio
   /gallery/audio/text_to_speech