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<section id="distillation-with-hubert">
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<h1>Distillation with HuBERT<a class="headerlink" href="#distillation-with-hubert" title="Permalink to this heading"></a></h1>
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<p>This tutorial shows you how to perform knowledge distillation in <a class="reference external" href="https://github.com/k2-fsa/icefall">icefall</a>
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with the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a> dataset. The distillation method
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used here is called “Multi Vector Quantization Knowledge Distillation” (MVQ-KD).
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Please have a look at our paper <a class="reference external" href="https://arxiv.org/abs/2211.00508">Predicting Multi-Codebook Vector Quantization Indexes for Knowledge Distillation</a>
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for more details about MVQ-KD.</p>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>This tutorial is based on recipe
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<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless4">pruned_transducer_stateless4</a>.
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Currently, we only implement MVQ-KD in this recipe. However, MVQ-KD is theoretically applicable to all recipes
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with only minor changes needed. Feel free to try out MVQ-KD in different recipes. If you
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encounter any problems, please open an issue here <a class="reference external" href="https://github.com/k2-fsa/icefall/issues">icefall</a>.</p>
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</div>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
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the environment for <a class="reference external" href="https://github.com/k2-fsa/icefall">icefall</a>.</p>
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</div>
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<div class="admonition hint">
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<p class="admonition-title">Hint</p>
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<p>We recommend you to use a GPU or several GPUs to run this recipe.</p>
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</div>
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<section id="data-preparation">
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<h2>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this heading"></a></h2>
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<p>We first prepare necessary training data for <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a>.
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This is the same as in <a class="reference internal" href="pruned_transducer_stateless.html#non-streaming-librispeech-pruned-transducer-stateless"><span class="std std-ref">Pruned transducer statelessX</span></a>.</p>
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<div class="admonition hint">
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<p class="admonition-title">Hint</p>
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<p>The data preparation is the same as other recipes on LibriSpeech dataset,
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if you have finished this step, you can skip to <a class="reference internal" href="#codebook-index-preparation"><span class="std std-ref">Codebook index preparation</span></a> directly.</p>
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</div>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
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$<span class="w"> </span>./prepare.sh
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</pre></div>
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</div>
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<p>The script <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> handles the data preparation for you, <strong>automagically</strong>.
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All you need to do is to run it.</p>
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<p>The data preparation contains several stages, you can use the following two
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options:</p>
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<blockquote>
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<div><ul class="simple">
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<li><p><code class="docutils literal notranslate"><span class="pre">--stage</span></code></p></li>
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<li><p><code class="docutils literal notranslate"><span class="pre">--stop_stage</span></code></p></li>
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</ul>
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</div></blockquote>
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<p>to control which stage(s) should be run. By default, all stages are executed.</p>
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<p>For example,</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
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$<span class="w"> </span>./prepare.sh<span class="w"> </span>--stage<span class="w"> </span><span class="m">0</span><span class="w"> </span>--stop_stage<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="c1"># run only stage 0</span>
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$<span class="w"> </span>./prepare.sh<span class="w"> </span>--stage<span class="w"> </span><span class="m">2</span><span class="w"> </span>--stop_stage<span class="w"> </span><span class="m">5</span><span class="w"> </span><span class="c1"># run from stage 2 to stage 5</span>
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</pre></div>
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</div>
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<div class="admonition hint">
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<p class="admonition-title">Hint</p>
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<p>If you have pre-downloaded the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a>
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dataset and the <a class="reference external" href="http://www.openslr.org/17/">musan</a> dataset, say,
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they are saved in <code class="docutils literal notranslate"><span class="pre">/tmp/LibriSpeech</span></code> and <code class="docutils literal notranslate"><span class="pre">/tmp/musan</span></code>, you can modify
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the <code class="docutils literal notranslate"><span class="pre">dl_dir</span></code> variable in <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> to point to <code class="docutils literal notranslate"><span class="pre">/tmp</span></code> so that
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<code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> won’t re-download them.</p>
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</div>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>All generated files by <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code>, e.g., features, lexicon, etc,
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are saved in <code class="docutils literal notranslate"><span class="pre">./data</span></code> directory.</p>
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</div>
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<p>We provide the following YouTube video showing how to run <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code>.</p>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>To get the latest news of <a class="reference external" href="https://github.com/k2-fsa">next-gen Kaldi</a>, please subscribe
|
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the following YouTube channel by <a class="reference external" href="https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw">Nadira Povey</a>:</p>
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<blockquote>
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<div><p><a class="reference external" href="https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw">https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw</a></p>
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</div></blockquote>
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</div>
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<div class="video_wrapper" style="">
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<iframe allowfullscreen="true" src="https://www.youtube.com/embed/ofEIoJL-mGM" style="border: 0; height: 345px; width: 560px">
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</iframe></div></section>
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<section id="codebook-index-preparation">
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<span id="id1"></span><h2>Codebook index preparation<a class="headerlink" href="#codebook-index-preparation" title="Permalink to this heading"></a></h2>
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<p>Here, we prepare necessary data for MVQ-KD. This requires the generation
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of codebook indexes (please read our <a class="reference external" href="https://arxiv.org/abs/2211.00508">paper</a>.
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if you are interested in details). In this tutorial, we use the pre-computed
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codebook indexes for convenience. The only thing you need to do is to
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run <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/distillation_with_hubert.sh">./distillation_with_hubert.sh</a>.</p>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>There are 5 stages in total, the first and second stage will be automatically skipped
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when choosing to downloaded codebook indexes prepared by <a class="reference external" href="https://github.com/k2-fsa/icefall">icefall</a>.
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Of course, you can extract and compute the codebook indexes by yourself. This
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will require you downloading a HuBERT-XL model and it can take a while for
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the extraction of codebook indexes.</p>
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</div>
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<p>As usual, you can control the stages you want to run by specifying the following
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two options:</p>
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<blockquote>
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<div><ul class="simple">
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<li><p><code class="docutils literal notranslate"><span class="pre">--stage</span></code></p></li>
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<li><p><code class="docutils literal notranslate"><span class="pre">--stop_stage</span></code></p></li>
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</ul>
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</div></blockquote>
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<p>For example,</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
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$<span class="w"> </span>./distillation_with_hubert.sh<span class="w"> </span>--stage<span class="w"> </span><span class="m">0</span><span class="w"> </span>--stop_stage<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="c1"># run only stage 0</span>
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$<span class="w"> </span>./distillation_with_hubert.sh<span class="w"> </span>--stage<span class="w"> </span><span class="m">2</span><span class="w"> </span>--stop_stage<span class="w"> </span><span class="m">4</span><span class="w"> </span><span class="c1"># run from stage 2 to stage 5</span>
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</pre></div>
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</div>
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<p>Here are a few options in <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/distillation_with_hubert.sh">./distillation_with_hubert.sh</a>
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you need to know before you proceed.</p>
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<ul class="simple">
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<li><p><code class="docutils literal notranslate"><span class="pre">--full_libri</span></code> If True, use full 960h data. Otherwise only <code class="docutils literal notranslate"><span class="pre">train-clean-100</span></code> will be used</p></li>
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<li><p><code class="docutils literal notranslate"><span class="pre">--use_extracted_codebook</span></code> If True, the first two stages will be skipped and the codebook
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indexes uploaded by us will be downloaded.</p></li>
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</ul>
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<p>Since we are using the pre-computed codebook indexes, we set
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<code class="docutils literal notranslate"><span class="pre">use_extracted_codebook=True</span></code>. If you want to do full <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a>
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experiments, please set <code class="docutils literal notranslate"><span class="pre">full_libri=True</span></code>.</p>
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<p>The following command downloads the pre-computed codebook indexes
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and prepares MVQ-augmented training manifests.</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>./distillation_with_hubert.sh<span class="w"> </span>--stage<span class="w"> </span><span class="m">2</span><span class="w"> </span>--stop_stage<span class="w"> </span><span class="m">2</span><span class="w"> </span><span class="c1"># run only stage 2</span>
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</pre></div>
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</div>
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<p>Please see the
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following screenshot for the output of an example execution.</p>
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<figure class="align-center" id="id5">
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<a class="reference internal image-reference" href="../../../_images/distillation_codebook.png"><img alt="Downloading codebook indexes and preparing training manifest." src="../../../_images/distillation_codebook.png" style="width: 800px;" /></a>
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<figcaption>
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<p><span class="caption-number">Fig. 6 </span><span class="caption-text">Downloading codebook indexes and preparing training manifest.</span><a class="headerlink" href="#id5" title="Permalink to this image"></a></p>
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</figcaption>
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</figure>
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<div class="admonition hint">
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<p class="admonition-title">Hint</p>
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<p>The codebook indexes we prepared for you in this tutorial
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are extracted from the 36-th layer of a fine-tuned HuBERT-XL model
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with 8 codebooks. If you want to try other configurations, please
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set <code class="docutils literal notranslate"><span class="pre">use_extracted_codebook=False</span></code> and set <code class="docutils literal notranslate"><span class="pre">embedding_layer</span></code> and
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<code class="docutils literal notranslate"><span class="pre">num_codebooks</span></code> by yourself.</p>
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</div>
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<p>Now, you should see the following files under the directory <code class="docutils literal notranslate"><span class="pre">./data/vq_fbank_layer36_cb8</span></code>.</p>
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<figure class="align-center" id="id6">
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<a class="reference internal image-reference" href="../../../_images/distillation_directory.png"><img alt="MVQ-augmented training manifests" src="../../../_images/distillation_directory.png" style="width: 800px;" /></a>
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<figcaption>
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<p><span class="caption-number">Fig. 7 </span><span class="caption-text">MVQ-augmented training manifests.</span><a class="headerlink" href="#id6" title="Permalink to this image"></a></p>
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</figcaption>
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</figure>
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<p>Whola! You are ready to perform knowledge distillation training now!</p>
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</section>
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<section id="training">
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<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading"></a></h2>
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<p>To perform training, please run stage 3 by executing the following command.</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>./prepare.sh<span class="w"> </span>--stage<span class="w"> </span><span class="m">3</span><span class="w"> </span>--stop_stage<span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="c1"># run MVQ training</span>
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</pre></div>
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</div>
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<p>Here is the code snippet for training:</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">WORLD_SIZE</span><span class="o">=</span><span class="k">$(</span><span class="nb">echo</span><span class="w"> </span><span class="si">${</span><span class="nv">CUDA_VISIBLE_DEVICES</span><span class="si">}</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>awk<span class="w"> </span><span class="s1">'{n=split($1, _, ","); print n}'</span><span class="k">)</span>
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|
||
./pruned_transducer_stateless6/train.py<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--manifest-dir<span class="w"> </span>./data/vq_fbank_layer36_cb8<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--master-port<span class="w"> </span><span class="m">12359</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--full-libri<span class="w"> </span><span class="nv">$full_libri</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--spec-aug-time-warp-factor<span class="w"> </span>-1<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">300</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--world-size<span class="w"> </span><span class="si">${</span><span class="nv">WORLD_SIZE</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--num-epochs<span class="w"> </span><span class="m">30</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--exp-dir<span class="w"> </span><span class="nv">$exp_dir</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--enable-distillation<span class="w"> </span>True<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--codebook-loss-scale<span class="w"> </span><span class="m">0</span>.01
|
||
</pre></div>
|
||
</div>
|
||
<p>There are a few training arguments in the following
|
||
training commands that should be paid attention to.</p>
|
||
<blockquote>
|
||
<div><ul class="simple">
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">--enable-distillation</span></code> If True, knowledge distillation training is enabled.</p></li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">--codebook-loss-scale</span></code> The scale of the knowledge distillation loss.</p></li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">--manifest-dir</span></code> The path to the MVQ-augmented manifest.</p></li>
|
||
</ul>
|
||
</div></blockquote>
|
||
</section>
|
||
<section id="decoding">
|
||
<h2>Decoding<a class="headerlink" href="#decoding" title="Permalink to this heading"></a></h2>
|
||
<p>After training finished, you can test the performance on using
|
||
the following command.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="m">0</span>
|
||
./pruned_transducer_stateless6/train.py<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--decoding-method<span class="w"> </span><span class="s2">"modified_beam_search"</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--epoch<span class="w"> </span><span class="m">30</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--avg<span class="w"> </span><span class="m">10</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">200</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--exp-dir<span class="w"> </span><span class="nv">$exp_dir</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--enable-distillation<span class="w"> </span>True
|
||
</pre></div>
|
||
</div>
|
||
<p>You should get similar results as <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS-100hours.md#distillation-with-hubert">here</a>.</p>
|
||
<p>That’s all! Feel free to experiment with your own setups and report your results.
|
||
If you encounter any problems during training, please open up an issue <a class="reference external" href="https://github.com/k2-fsa/icefall/issues">here</a>.</p>
|
||
</section>
|
||
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|
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