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---
title: Core
keywords: fastai
sidebar: home_sidebar
summary: "Core functionality for the fastai audio library."
description: "Core functionality for the fastai audio library."
---
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<h1 id="Audio-Signals">Audio Signals<a class="anchor-link" href="#Audio-Signals"> </a></h1>
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<h2 id="AudioGetter">AudioGetter<a class="anchor-link" href="#AudioGetter"> </a></h2>
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<p>This section regroups the basic types used in vision with the transform that create objects of those types.</p>
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<h4 id="get_audio_files" class="doc_header"><code>get_audio_files</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L26" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_audio_files</code>(<strong><code>path</code></strong>, <strong><code>recurse</code></strong>=<em><code>True</code></em>, <strong><code>folders</code></strong>=<em><code>None</code></em>)</p>
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<p>Get image files in <code>path</code> recursively, only in <code>folders</code>, if specified.</p>
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<h4 id="AudioGetter" class="doc_header"><code>AudioGetter</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L31" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>AudioGetter</code>(<strong><code>suf</code></strong>=<em><code>''</code></em>, <strong><code>recurse</code></strong>=<em><code>True</code></em>, <strong><code>folders</code></strong>=<em><code>None</code></em>)</p>
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<p>Create <code>get_image_files</code> partial function that searches path suffix <code>suf</code> and passes along <code>kwargs</code>, only in <code>folders</code>, if specified.</p>
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<h4 id="tar_extract_at_filename" class="doc_header"><code>tar_extract_at_filename</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L43" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>tar_extract_at_filename</code>(<strong><code>fname</code></strong>, <strong><code>dest</code></strong>)</p>
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<p>Extract <code>fname</code> to <code>dest</code>/<code>fname</code>.name folder using <code>tarfile</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">SPEAKERS10</span><span class="p">,</span> <span class="n">extract_func</span><span class="o">=</span><span class="n">tar_extract_at_filename</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">audio_getter</span> <span class="o">=</span> <span class="n">AudioGetter</span><span class="p">(</span><span class="s2">""</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">folders</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#files will load differently on different machines so we specify examples by name</span>
<span class="n">ex_files</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span><span class="o">/</span><span class="n">f</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'m0005_us_m0005_00218.wav'</span><span class="p">,</span>
<span class="s1">'f0003_us_f0003_00279.wav'</span><span class="p">,</span>
<span class="s1">'f0001_us_f0001_00168.wav'</span><span class="p">,</span>
<span class="s1">'f0005_us_f0005_00286.wav'</span><span class="p">,]]</span>
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<h2 id="AudioItem">AudioItem<a class="anchor-link" href="#AudioItem"> </a></h2>
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<h2 id="AudioTensor" class="doc_header"><code>class</code> <code>AudioTensor</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L49" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>AudioTensor</code>(<strong><code>x</code></strong>, <strong><code>sr</code></strong>, <strong>**<code>kwargs</code></strong>) :: <code>TensorBase</code></p>
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<p>Patch on indexing to retain the AudioTensor type, so when indexing it stays the same type</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">AudioTensor</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span> <span class="n">sr</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
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<h4 id="show_audio_signal" class="doc_header"><code>show_audio_signal</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L90" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>show_audio_signal</code>(<strong><code>ai</code></strong>, <strong><code>ctx</code></strong>, <strong>**<code>kwargs</code></strong>)</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">item0</span><span class="o">.</span><span class="n">sr</span><span class="p">,</span> <span class="n">item0</span><span class="o">.</span><span class="n">nchannels</span><span class="p">,</span> <span class="n">item0</span><span class="o">.</span><span class="n">nsamples</span><span class="p">,</span> <span class="n">item0</span><span class="o">.</span><span class="n">duration</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">item0</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">item1</span> <span class="o">=</span> <span class="n">AudioTensor</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">files</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">item0</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">item1</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#get 3 equal length portions of 3 different signals so we can stack them</span>
<span class="c1">#for a fake multichannel example</span>
<span class="n">ai0</span><span class="p">,</span> <span class="n">ai1</span><span class="p">,</span> <span class="n">ai2</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="n">AudioTensor</span><span class="o">.</span><span class="n">create</span><span class="p">,</span> <span class="n">ex_files</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]);</span>
<span class="n">min_samples</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">ai0</span><span class="o">.</span><span class="n">nsamples</span><span class="p">,</span> <span class="n">ai1</span><span class="o">.</span><span class="n">nsamples</span><span class="p">,</span> <span class="n">ai2</span><span class="o">.</span><span class="n">nsamples</span><span class="p">)</span>
<span class="n">s0</span><span class="p">,</span> <span class="n">s1</span><span class="p">,</span> <span class="n">s2</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[:,:</span><span class="n">min_samples</span><span class="p">],</span> <span class="p">(</span><span class="n">ai0</span><span class="p">,</span> <span class="n">ai1</span><span class="p">,</span> <span class="n">ai2</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tst0</span> <span class="o">=</span> <span class="n">AudioTensor</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span> <span class="n">sr</span><span class="o">=</span><span class="mi">120</span><span class="p">)</span>
<span class="n">tst1</span> <span class="o">=</span> <span class="n">AudioTensor</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span> <span class="n">sr</span><span class="o">=</span><span class="mi">150</span><span class="p">)</span>
<span class="p">(</span><span class="n">tst0</span> <span class="o">+</span> <span class="n">tst1</span><span class="p">)</span><span class="o">.</span><span class="n">sr</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">s0</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">s1</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">s2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fake_multichannel</span> <span class="o">=</span> <span class="n">AudioTensor</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">s0</span><span class="p">,</span> <span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">sr</span><span class="o">=</span><span class="mi">16000</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">fake_multichannel</span><span class="o">.</span><span class="n">nchannels</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">fake_multichannel</span><span class="o">.</span><span class="n">nsamples</span><span class="p">,</span> <span class="mi">53760</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fake_multichannel</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h2 id="OpenAudio" class="doc_header"><code>class</code> <code>OpenAudio</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L100" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>OpenAudio</code>(<strong><code>items</code></strong>) :: <code>Transform</code></p>
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<p>Delegates (<code>__call__</code>,<code>decode</code>,<code>setup</code>) to (<code>encodes</code>,<code>decodes</code>,<code>setups</code>) if <code>split_idx</code> matches</p>
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<p>repr of Transform is:<br>
classname: self.use_as_item {self.encodes} {self.decodes}<br>
encodes and decodes are TypeDispatches whose reprs are str of dict where k/v pair is typename and function that handles that type</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">oa</span> <span class="o">=</span> <span class="n">OpenAudio</span><span class="p">(</span><span class="n">files</span><span class="p">);</span> <span class="n">oa</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#demonstrate functionality of OpenAudio.encodes, the rest of the nb will</span>
<span class="c1">#use files that are opened by name for reproducibility/testing</span>
<span class="n">oa</span> <span class="o">=</span> <span class="n">OpenAudio</span><span class="p">(</span><span class="n">files</span><span class="p">)</span>
<span class="n">item100</span> <span class="o">=</span> <span class="n">oa</span><span class="o">.</span><span class="n">encodes</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="n">item100</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#test open audio on a random set of files</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">files</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">test_eq_type</span><span class="p">(</span><span class="n">oa</span><span class="o">.</span><span class="n">encodes</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="n">AudioTensor</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">files</span><span class="p">[</span><span class="n">idx</span><span class="p">]))</span>
<span class="n">test_eq_type</span><span class="p">(</span><span class="n">oa</span><span class="o">.</span><span class="n">decodes</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="n">files</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">oa</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">oa</span><span class="o">.</span><span class="n">encodes</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
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<h2 id="Create-functions-to-wrap-TorchAudio">Create functions to wrap TorchAudio<a class="anchor-link" href="#Create-functions-to-wrap-TorchAudio"> </a></h2>
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<h1 id="Add-AudioBlock">Add AudioBlock<a class="anchor-link" href="#Add-AudioBlock"> </a></h1>
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<h4 id="AudioBlock" class="doc_header"><code>AudioBlock</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L118" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>AudioBlock</code>()</p>
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<h1 id="Audio-Spectrograms">Audio Spectrograms<a class="anchor-link" href="#Audio-Spectrograms"> </a></h1>
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<div class='alert alert-block alert-warning'><strong>Note: </strong><br> Overriding getattr to store the settings isnt ideal, but if we dump them all in as attributes by doing `x.__dict__.update(settings)` we then can't easily pass settings when we do a transform and create a new AudioSpectrogram objct. Potential fixes are<br>
1. Having both a settings dict and updating the dict with all its attributes (this feels dirty)<br>
2. Finding a way to implement deepcopy for AudioSpectrogram so that we can clone it efficiently<br>
3. Dumping the spectrogram settings and having a method that collects them so it can be passed to the constructor when we make a new AudioSpectrogram object in a transform<br>
**Update: #2 is now a reasonable option because we mutate in place and dont need to pass settings forward**
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<h2 id="AudioSpectrogram-Class">AudioSpectrogram Class<a class="anchor-link" href="#AudioSpectrogram-Class"> </a></h2>
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<h2 id="AudioSpectrogram" class="doc_header"><code>class</code> <code>AudioSpectrogram</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L121" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>AudioSpectrogram</code>(<strong><code>x</code></strong>, <strong>**<code>kwargs</code></strong>) :: <code>TensorImageBase</code></p>
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<div class='alert alert-block alert-info'><strong>TO-DO:</strong><br>
1. Get colorbar and axes working for multiplot display <br>
2. Have someone who knows matplotlib better cleanup/refactor<br>
3. Plotting the spectrogram forces it to a uniform size, we may want to display either the
shape of the image, or display it to scale with something like plt.figure(figsize=(sg.width/30, sg.height/30))
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<h4 id="show_spectrogram" class="doc_header"><code>show_spectrogram</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L148" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>show_spectrogram</code>(<strong><code>sg</code></strong>, <strong><code>ax</code></strong>, <strong><code>ctx</code></strong>, <strong><code>figsize</code></strong>, <strong>**<code>kwargs</code></strong>)</p>
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<h2 id="Spectrogram-Generation:-AudioToSpec">Spectrogram Generation: AudioToSpec<a class="anchor-link" href="#Spectrogram-Generation:-AudioToSpec"> </a></h2>
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<h2 id="AudioToSpec" class="doc_header"><code>class</code> <code>AudioToSpec</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L183" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>AudioToSpec</code>(<strong><code>pipe</code></strong>, <strong><code>settings</code></strong>) :: <code>Transform</code></p>
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<p>Delegates (<code>__call__</code>,<code>decode</code>,<code>setup</code>) to (<code>encodes</code>,<code>decodes</code>,<code>setups</code>) if <code>split_idx</code> matches</p>
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<h4 id="SpectrogramTransformer" class="doc_header"><code>SpectrogramTransformer</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L199" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>SpectrogramTransformer</code>(<strong><code>mel</code></strong>=<em><code>True</code></em>, <strong><code>to_db</code></strong>=<em><code>True</code></em>)</p>
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<h4 id="fill_pipeline" class="doc_header"><code>fill_pipeline</code><a href="https://github.com/rbracco/fastai2_audio/tree/master/fastai2_audio/core.py#L214" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>fill_pipeline</code>(<strong><code>transform_list</code></strong>, <strong><code>sg_type</code></strong>, <strong>**<code>kwargs</code></strong>)</p>