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<!doctype html>
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<title>SpeechBrain: Speech Processing</title>
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<h2>SpeechBrain Tutorials</h2>
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<p> <img src="img/logo_discourse.png"/> Join our official <a href="https://speechbrain.discourse.group">Discourse</a> to discuss with SpeechBrain users coming from all around the world! <img src="img/logo_discourse.png"/></p>
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<a class="active" href="#">Speech Processing</a>
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<ul class="blog_meta list">
<li><a href="about.html">Ravanelli M.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Apr. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: medium<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 45min<i class="lnr lnr-hourglass"></i></a></li>
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<h2>Speech Recognition From Scratch</h2>
<p>Do you want to figure out how to implement your speech recognizer with SpeechBrain? Look no further, you're in the right place. This tutorial will walk you
through all the steps needed to implement an offline end-to-end attention-based speech recognizer. This is a self-contained tutorial that will help you "connecting the dots" across all the steps needed to train a modern speech recognizer. We will address data preparation, tokenizer training, language model, ASR model, and inference. We will explain how to train your model on your data.</p>
<a href="https://colab.research.google.com/drive/1aFgzrUv3udM_gNJNUoLaHIm78QHtxdIz?usp=sharing" class="blog_btn">Open in Google Colab</a>
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<li><a href="about.html">Plantinga P.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Feb. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: medium<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 30min<i class="lnr lnr-hourglass"></i></a></li>
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<h2>Speech Enhancement From Scratch</h2>
<p>So you want to do regression tasks with speech? Look no further, you're in the right place.
This tutorial will walk you through a basic speech enhancement template with SpeechBrain to
show all the components needed for making a new recipe.</p>
<a href="https://colab.research.google.com/drive/18RyiuKupAhwWX7fh3LCatwQGU5eIS3TR?usp=sharing" class="blog_btn">Open in Google Colab</a>
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<article class="row blog_item">
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<a class="active" href="#">Speech Processing</a>
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<ul class="blog_meta list">
<li><a href="about.html">Ravanelli M.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Jan. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: easy<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 20min<i class="lnr lnr-hourglass"></i></a></li>
</ul>
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<div class="col-md-9">
<div class="blog_post">
<div class="blog_details">
<h2>Speech Augmentation</h2>
<p>A popular saying in machine learning is "there is no better data than more data". However, collecting new data can be expensive
and we must cleverly use the available dataset. One popular technique is called speech augmentation. The idea is to artificially
corrupt the original speech signals to give the network the "illusion" that we are processing a new signal. This acts as a powerful regularizer,
that normally helps neural networks improving generalization and thus achieve better performance on test data.</p>
<a href="https://colab.research.google.com/drive/1JJc4tBhHNXRSDM2xbQ3Z0jdDQUw4S5lr?usp=sharing" class="blog_btn">Open in Google Colab</a>
</div>
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</article>
<article class="row blog_item">
<div class="col-md-3">
<div class="blog_info text-right">
<div class="post_tag">
<a class="active" href="#">Speech Processing</a>
</div>
<ul class="blog_meta list">
<li><a href="about.html">Ravanelli M.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Jan. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: easy<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 20min<i class="lnr lnr-hourglass"></i></a></li>
</ul>
</div>
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<div class="col-md-9">
<div class="blog_post">
<div class="blog_details">
<h2>Fourier Transform and Spectrograms</h2>
<p>In speech and audio processing, the signal in the time-domain is often transformed into another domain.
Ok, but why do we need to transform an audio signal? Some speech characteristics/patterns of the signal (e.g, pitch, formats)
might not be very evident when looking at the audio in the time-domain. With properly designed transformations,
it might be easier to extract the needed information from the signal itself. The most popular transformation is the
Fourier Transform, which turns the time-domain signal into an equivalent representation in the frequency domain.
In the following sections, we will describe the Fourier transforms along with other related transformations such as
Short-Term Fourier Transform (STFT) and spectrograms.</p>
<a href="https://colab.research.google.com/drive/18IgBv3Ip0rWXjYoZywttSmW7Y2AIK1vJ?usp=sharing" class="blog_btn">Open in Google Colab</a>
</div>
</div>
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</article>
<article class="row blog_item">
<div class="col-md-3">
<div class="blog_info text-right">
<div class="post_tag">
<a class="active" href="#">Speech Processing</a>
</div>
<ul class="blog_meta list">
<li><a href="about.html">Ravanelli M.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Jan. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: easy<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 20min<i class="lnr lnr-hourglass"></i></a></li>
</ul>
</div>
</div>
<div class="col-md-9">
<div class="blog_post">
<div class="blog_details">
<h2>Speech Features</h2>
<p>Speech is a very high-dimensional signal. For instance, when the sampling frequency is 16 kHz,
we have 16000 samples for each second. Working with such very high dimensional data can be critical from a machine learning perspective.
The goal of feature extraction is to find more compact ways to represent speech.</p>
<a href="https://colab.research.google.com/drive/1CI72Xyay80mmmagfLaIIeRoDgswWHT_g?usp=sharing" class="blog_btn">Open in Google Colab</a>
</div>
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<article class="row blog_item">
<div class="col-md-3">
<div class="blog_info text-right">
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<li><a href="about.html">Ravanelli M.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Feb. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: medium<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 20min<i class="lnr lnr-hourglass"></i></a></li>
</ul>
</div>
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<div class="col-md-9">
<div class="blog_post">
<div class="blog_details">
<h2>Environmental corruption</h2>
<p>In realistic speech processing applications, the signal recorded by the microphone is corrupted by noise and reverberation.
This is particularly harmful in distant-talking (far-field) scenarios, where the speaker and the reference microphone are distant
(think about popular devices such as Google Home, Amazon Echo, Kinect, and similar devices).</p>
<a href="https://colab.research.google.com/drive/1mAimqZndq0BwQj63VcDTr6_uCMC6i6Un?usp=sharing" class="blog_btn">Open in Google Colab</a>
</div>
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</article>
<article class="row blog_item">
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<div class="blog_info text-right">
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<a class="active" href="#">Speech Processing</a>
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<li><a href="about.html">Ravanelli M.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Jan. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: medium<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 30min<i class="lnr lnr-hourglass"></i></a></li>
</ul>
</div>
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<div class="col-md-9">
<div class="blog_post">
<div class="blog_details">
<h2>Source Separation</h2>
<p>In source separation, the goal is to be able to separate out the sources from an observed mixture signal
which consists of superposition of several sources. In this tutorial, we cover few examples of performing source separation with SpeechBrain.</p>
<a href="https://colab.research.google.com/drive/1YxsMW1KNqP1YihNUcfrjy0zUp7FhNNhN?usp=sharing" class="blog_btn">Open in Google Colab</a>
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<ul class="blog_meta list">
<li><a href="about.html">Grondin F. & Aris W.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Jan. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: medium<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 20min<i class="lnr lnr-hourglass"></i></a></li>
</ul>
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<h2>Multi-microphone Beamforming</h2>
<p>Using a microphone array can be very handy to improve the signal quality
(e.g. reduce reverberation and noise) prior to performing speech recognition tasks.
Microphone arrays can also estimate the direction of arrival of a sound source, and this information can later
be used to "listen" in the direction of the source of interest.</p>
<a href="https://colab.research.google.com/drive/1UVoYDUiIrwMpBTghQPbA6rC1mc9IBzi6?usp=sharing" class="blog_btn">Open in Google Colab</a>
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<p style="text-align:justify">SpeechBrain isn't a company or an association.
It is an open-source toolkit and a community created by Dr. Mirco Ravanelli and co-created by Dr. Titouan Parcollet.
We aim at making speech technologies more accessible for the community. </p>
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