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<section id="pruned-transducer-statelessx">
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<h1>Pruned transducer statelessX<a class="headerlink" href="#pruned-transducer-statelessx" title="Permalink to this heading"></a></h1>
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<p>This tutorial shows you how to run a conformer transducer model
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with the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a> dataset.</p>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>The tutorial is suitable for <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless">pruned_transducer_stateless</a>,
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<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless2">pruned_transducer_stateless2</a>,
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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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<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless5">pruned_transducer_stateless5</a>,
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We will take pruned_transducer_stateless4 as an example in this tutorial.</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 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 <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</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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<div class="admonition hint">
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<p class="admonition-title">Hint</p>
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<p>Please scroll down to the bottom of this page to find download links
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for pretrained models if you don’t want to train a model from scratch.</p>
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</div>
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<p>We use pruned RNN-T to compute the loss.</p>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>You can find the paper about pruned RNN-T at the following address:</p>
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<p><a class="reference external" href="https://arxiv.org/abs/2206.13236">https://arxiv.org/abs/2206.13236</a></p>
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</div>
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<p>The transducer model consists of 3 parts:</p>
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<blockquote>
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<div><ul class="simple">
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<li><p>Encoder, a.k.a, the transcription network. We use a Conformer model (the reworked version by Daniel Povey)</p></li>
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<li><p>Decoder, a.k.a, the prediction network. We use a stateless model consisting of
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<code class="docutils literal notranslate"><span class="pre">nn.Embedding</span></code> and <code class="docutils literal notranslate"><span class="pre">nn.Conv1d</span></code></p></li>
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<li><p>Joiner, a.k.a, the joint network.</p></li>
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</ul>
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</div></blockquote>
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<div class="admonition caution">
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<p class="admonition-title">Caution</p>
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<p>Contrary to the conventional RNN-T models, we use a stateless decoder.
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That is, it has no recurrent connections.</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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<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 <code class="docutils literal notranslate"><span class="pre">Training</span></code> 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>
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</pre></div>
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</div>
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<p>means to run only stage 0.</p>
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<p>To run stage 2 to stage 5, use:</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">2</span><span class="w"> </span>--stop-stage<span class="w"> </span><span class="m">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="training">
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<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading"></a></h2>
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<section id="configurable-options">
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<h3>Configurable options<a class="headerlink" href="#configurable-options" title="Permalink to this heading"></a></h3>
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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>./pruned_transducer_stateless4/train.py<span class="w"> </span>--help
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</pre></div>
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</div>
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<p>shows you the training options that can be passed from the commandline.
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The following options are used quite often:</p>
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<blockquote>
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<div><ul>
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<li><p><code class="docutils literal notranslate"><span class="pre">--exp-dir</span></code></p>
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<p>The directory to save checkpoints, training logs and tensorboard.</p>
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</li>
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<li><p><code class="docutils literal notranslate"><span class="pre">--full-libri</span></code></p>
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<p>If it’s True, the training part uses all the training data, i.e.,
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960 hours. Otherwise, the training part uses only the subset
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<code class="docutils literal notranslate"><span class="pre">train-clean-100</span></code>, which has 100 hours of training data.</p>
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<div class="admonition caution">
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<p class="admonition-title">Caution</p>
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<p>The training set is perturbed by speed with two factors: 0.9 and 1.1.
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If <code class="docutils literal notranslate"><span class="pre">--full-libri</span></code> is True, each epoch actually processes
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<code class="docutils literal notranslate"><span class="pre">3x960</span> <span class="pre">==</span> <span class="pre">2880</span></code> hours of data.</p>
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</div>
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</li>
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<li><p><code class="docutils literal notranslate"><span class="pre">--num-epochs</span></code></p>
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<p>It is the number of epochs to train. For instance,
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<code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/train.py</span> <span class="pre">--num-epochs</span> <span class="pre">30</span></code> trains for 30 epochs
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and generates <code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …, <code class="docutils literal notranslate"><span class="pre">epoch-30.pt</span></code>
|
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in the folder <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/exp</span></code>.</p>
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</li>
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<li><p><code class="docutils literal notranslate"><span class="pre">--start-epoch</span></code></p>
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<p>It’s used to resume training.
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<code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/train.py</span> <span class="pre">--start-epoch</span> <span class="pre">10</span></code> loads the
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||
checkpoint <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/exp/epoch-9.pt</span></code> and starts
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training from epoch 10, based on the state from epoch 9.</p>
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</li>
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<li><p><code class="docutils literal notranslate"><span class="pre">--world-size</span></code></p>
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<p>It is used for multi-GPU single-machine DDP training.</p>
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<blockquote>
|
||
<div><ul class="simple">
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<li><ol class="loweralpha simple">
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<li><p>If it is 1, then no DDP training is used.</p></li>
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</ol>
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</li>
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<li><ol class="loweralpha simple" start="2">
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<li><p>If it is 2, then GPU 0 and GPU 1 are used for DDP training.</p></li>
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</ol>
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</li>
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</ul>
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</div></blockquote>
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<p>The following shows some use cases with it.</p>
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<blockquote>
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<div><p><strong>Use case 1</strong>: You have 4 GPUs, but you only want to use GPU 0 and
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GPU 2 for training. You can do the following:</p>
|
||
<blockquote>
|
||
<div><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
|
||
$<span class="w"> </span><span class="nb">export</span><span class="w"> </span><span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">"0,2"</span>
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$<span class="w"> </span>./pruned_transducer_stateless4/train.py<span class="w"> </span>--world-size<span class="w"> </span><span class="m">2</span>
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||
</pre></div>
|
||
</div>
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||
</div></blockquote>
|
||
<p><strong>Use case 2</strong>: You have 4 GPUs and you want to use all of them
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for training. You can do the following:</p>
|
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<blockquote>
|
||
<div><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>./pruned_transducer_stateless4/train.py<span class="w"> </span>--world-size<span class="w"> </span><span class="m">4</span>
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||
</pre></div>
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||
</div>
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</div></blockquote>
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<p><strong>Use case 3</strong>: You have 4 GPUs but you only want to use GPU 3
|
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for training. You can do the following:</p>
|
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<blockquote>
|
||
<div><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><span class="nb">export</span><span class="w"> </span><span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">"3"</span>
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$<span class="w"> </span>./pruned_transducer_stateless4/train.py<span class="w"> </span>--world-size<span class="w"> </span><span class="m">1</span>
|
||
</pre></div>
|
||
</div>
|
||
</div></blockquote>
|
||
</div></blockquote>
|
||
<div class="admonition caution">
|
||
<p class="admonition-title">Caution</p>
|
||
<p>Only multi-GPU single-machine DDP training is implemented at present.
|
||
Multi-GPU multi-machine DDP training will be added later.</p>
|
||
</div>
|
||
</li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">--max-duration</span></code></p>
|
||
<p>It specifies the number of seconds over all utterances in a
|
||
batch, before <strong>padding</strong>.
|
||
If you encounter CUDA OOM, please reduce it.</p>
|
||
<div class="admonition hint">
|
||
<p class="admonition-title">Hint</p>
|
||
<p>Due to padding, the number of seconds of all utterances in a
|
||
batch will usually be larger than <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code>.</p>
|
||
<p>A larger value for <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code> may cause OOM during training,
|
||
while a smaller value may increase the training time. You have to
|
||
tune it.</p>
|
||
</div>
|
||
</li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">--use-fp16</span></code></p>
|
||
<p>If it is True, the model will train with half precision, from our experiment
|
||
results, by using half precision you can train with two times larger <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code>
|
||
so as to get almost 2X speed up.</p>
|
||
</li>
|
||
</ul>
|
||
</div></blockquote>
|
||
</section>
|
||
<section id="pre-configured-options">
|
||
<h3>Pre-configured options<a class="headerlink" href="#pre-configured-options" title="Permalink to this heading"></a></h3>
|
||
<p>There are some training options, e.g., number of encoder layers,
|
||
encoder dimension, decoder dimension, number of warmup steps etc,
|
||
that are not passed from the commandline.
|
||
They are pre-configured by the function <code class="docutils literal notranslate"><span class="pre">get_params()</span></code> in
|
||
<a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless4/train.py">pruned_transducer_stateless4/train.py</a></p>
|
||
<p>You don’t need to change these pre-configured parameters. If you really need to change
|
||
them, please modify <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/train.py</span></code> directly.</p>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>The options for <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless5/train.py">pruned_transducer_stateless5</a> are a little different from
|
||
other recipes. It allows you to configure <code class="docutils literal notranslate"><span class="pre">--num-encoder-layers</span></code>, <code class="docutils literal notranslate"><span class="pre">--dim-feedforward</span></code>, <code class="docutils literal notranslate"><span class="pre">--nhead</span></code>, <code class="docutils literal notranslate"><span class="pre">--encoder-dim</span></code>, <code class="docutils literal notranslate"><span class="pre">--decoder-dim</span></code>, <code class="docutils literal notranslate"><span class="pre">--joiner-dim</span></code> from commandline, so that you can train models with different size with pruned_transducer_stateless5.</p>
|
||
</div>
|
||
</section>
|
||
<section id="training-logs">
|
||
<h3>Training logs<a class="headerlink" href="#training-logs" title="Permalink to this heading"></a></h3>
|
||
<p>Training logs and checkpoints are saved in <code class="docutils literal notranslate"><span class="pre">--exp-dir</span></code> (e.g. <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/exp</span></code>.
|
||
You will find the following files in that directory:</p>
|
||
<blockquote>
|
||
<div><ul>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …</p>
|
||
<p>These are checkpoint files saved at the end of each epoch, containing model
|
||
<code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and optimizer <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.
|
||
To resume training from some checkpoint, say <code class="docutils literal notranslate"><span class="pre">epoch-10.pt</span></code>, you can use:</p>
|
||
<blockquote>
|
||
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>./pruned_transducer_stateless4/train.py<span class="w"> </span>--start-epoch<span class="w"> </span><span class="m">11</span>
|
||
</pre></div>
|
||
</div>
|
||
</div></blockquote>
|
||
</li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">checkpoint-436000.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">checkpoint-438000.pt</span></code>, …</p>
|
||
<p>These are checkpoint files saved every <code class="docutils literal notranslate"><span class="pre">--save-every-n</span></code> batches,
|
||
containing model <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and optimizer <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.
|
||
To resume training from some checkpoint, say <code class="docutils literal notranslate"><span class="pre">checkpoint-436000</span></code>, you can use:</p>
|
||
<blockquote>
|
||
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>./pruned_transducer_stateless4/train.py<span class="w"> </span>--start-batch<span class="w"> </span><span class="m">436000</span>
|
||
</pre></div>
|
||
</div>
|
||
</div></blockquote>
|
||
</li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">tensorboard/</span></code></p>
|
||
<p>This folder contains tensorBoard logs. Training loss, validation loss, learning
|
||
rate, etc, are recorded in these logs. You can visualize them by:</p>
|
||
<blockquote>
|
||
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>pruned_transducer_stateless4/exp/tensorboard
|
||
$<span class="w"> </span>tensorboard<span class="w"> </span>dev<span class="w"> </span>upload<span class="w"> </span>--logdir<span class="w"> </span>.<span class="w"> </span>--description<span class="w"> </span><span class="s2">"pruned transducer training for LibriSpeech with icefall"</span>
|
||
</pre></div>
|
||
</div>
|
||
</div></blockquote>
|
||
<p>It will print something like below:</p>
|
||
<blockquote>
|
||
<div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TensorFlow</span> <span class="n">installation</span> <span class="ow">not</span> <span class="n">found</span> <span class="o">-</span> <span class="n">running</span> <span class="k">with</span> <span class="n">reduced</span> <span class="n">feature</span> <span class="nb">set</span><span class="o">.</span>
|
||
<span class="n">Upload</span> <span class="n">started</span> <span class="ow">and</span> <span class="n">will</span> <span class="k">continue</span> <span class="n">reading</span> <span class="nb">any</span> <span class="n">new</span> <span class="n">data</span> <span class="k">as</span> <span class="n">it</span><span class="s1">'s added to the logdir.</span>
|
||
|
||
<span class="n">To</span> <span class="n">stop</span> <span class="n">uploading</span><span class="p">,</span> <span class="n">press</span> <span class="n">Ctrl</span><span class="o">-</span><span class="n">C</span><span class="o">.</span>
|
||
|
||
<span class="n">New</span> <span class="n">experiment</span> <span class="n">created</span><span class="o">.</span> <span class="n">View</span> <span class="n">your</span> <span class="n">TensorBoard</span> <span class="n">at</span><span class="p">:</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">tensorboard</span><span class="o">.</span><span class="n">dev</span><span class="o">/</span><span class="n">experiment</span><span class="o">/</span><span class="n">QOGSPBgsR8KzcRMmie9JGw</span><span class="o">/</span>
|
||
|
||
<span class="p">[</span><span class="mi">2022</span><span class="o">-</span><span class="mi">11</span><span class="o">-</span><span class="mi">20</span><span class="n">T15</span><span class="p">:</span><span class="mi">50</span><span class="p">:</span><span class="mi">50</span><span class="p">]</span> <span class="n">Started</span> <span class="n">scanning</span> <span class="n">logdir</span><span class="o">.</span>
|
||
<span class="n">Uploading</span> <span class="mi">4468</span> <span class="n">scalars</span><span class="o">...</span>
|
||
<span class="p">[</span><span class="mi">2022</span><span class="o">-</span><span class="mi">11</span><span class="o">-</span><span class="mi">20</span><span class="n">T15</span><span class="p">:</span><span class="mi">53</span><span class="p">:</span><span class="mi">02</span><span class="p">]</span> <span class="n">Total</span> <span class="n">uploaded</span><span class="p">:</span> <span class="mi">210171</span> <span class="n">scalars</span><span class="p">,</span> <span class="mi">0</span> <span class="n">tensors</span><span class="p">,</span> <span class="mi">0</span> <span class="n">binary</span> <span class="n">objects</span>
|
||
<span class="n">Listening</span> <span class="k">for</span> <span class="n">new</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">logdir</span><span class="o">...</span>
|
||
</pre></div>
|
||
</div>
|
||
</div></blockquote>
|
||
<p>Note there is a URL in the above output. Click it and you will see
|
||
the following screenshot:</p>
|
||
<blockquote>
|
||
<div><figure class="align-center" id="id7">
|
||
<a class="reference external image-reference" href="https://tensorboard.dev/experiment/QOGSPBgsR8KzcRMmie9JGw/"><img alt="TensorBoard screenshot" src="../../../_images/librispeech-pruned-transducer-tensorboard-log.jpg" style="width: 600px;" /></a>
|
||
<figcaption>
|
||
<p><span class="caption-number">Fig. 5 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id7" title="Permalink to this image"></a></p>
|
||
</figcaption>
|
||
</figure>
|
||
</div></blockquote>
|
||
</li>
|
||
</ul>
|
||
<div class="admonition hint">
|
||
<p class="admonition-title">Hint</p>
|
||
<p>If you don’t have access to google, you can use the following command
|
||
to view the tensorboard log locally:</p>
|
||
<blockquote>
|
||
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>pruned_transducer_stateless4/exp/tensorboard
|
||
tensorboard<span class="w"> </span>--logdir<span class="w"> </span>.<span class="w"> </span>--port<span class="w"> </span><span class="m">6008</span>
|
||
</pre></div>
|
||
</div>
|
||
</div></blockquote>
|
||
<p>It will print the following message:</p>
|
||
<blockquote>
|
||
<div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Serving</span> <span class="n">TensorBoard</span> <span class="n">on</span> <span class="n">localhost</span><span class="p">;</span> <span class="n">to</span> <span class="n">expose</span> <span class="n">to</span> <span class="n">the</span> <span class="n">network</span><span class="p">,</span> <span class="n">use</span> <span class="n">a</span> <span class="n">proxy</span> <span class="ow">or</span> <span class="k">pass</span> <span class="o">--</span><span class="n">bind_all</span>
|
||
<span class="n">TensorBoard</span> <span class="mf">2.8.0</span> <span class="n">at</span> <span class="n">http</span><span class="p">:</span><span class="o">//</span><span class="n">localhost</span><span class="p">:</span><span class="mi">6008</span><span class="o">/</span> <span class="p">(</span><span class="n">Press</span> <span class="n">CTRL</span><span class="o">+</span><span class="n">C</span> <span class="n">to</span> <span class="n">quit</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
</div></blockquote>
|
||
<p>Now start your browser and go to <a class="reference external" href="http://localhost:6008">http://localhost:6008</a> to view the tensorboard
|
||
logs.</p>
|
||
</div>
|
||
<ul>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">log/log-train-xxxx</span></code></p>
|
||
<p>It is the detailed training log in text format, same as the one
|
||
you saw printed to the console during training.</p>
|
||
</li>
|
||
</ul>
|
||
</div></blockquote>
|
||
</section>
|
||
<section id="usage-example">
|
||
<h3>Usage example<a class="headerlink" href="#usage-example" title="Permalink to this heading"></a></h3>
|
||
<p>You can use the following command to start the training using 6 GPUs:</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="s2">"0,1,2,3,4,5"</span>
|
||
./pruned_transducer_stateless4/train.py<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--world-size<span class="w"> </span><span class="m">6</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>--start-epoch<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--exp-dir<span class="w"> </span>pruned_transducer_stateless4/exp<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--full-libri<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">300</span>
|
||
</pre></div>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<section id="decoding">
|
||
<h2>Decoding<a class="headerlink" href="#decoding" title="Permalink to this heading"></a></h2>
|
||
<p>The decoding part uses checkpoints saved by the training part, so you have
|
||
to run the training part first.</p>
|
||
<div class="admonition hint">
|
||
<p class="admonition-title">Hint</p>
|
||
<p>There are two kinds of checkpoints:</p>
|
||
<blockquote>
|
||
<div><ul class="simple">
|
||
<li><p>(1) <code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …, which are saved at the end
|
||
of each epoch. You can pass <code class="docutils literal notranslate"><span class="pre">--epoch</span></code> to
|
||
<code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/decode.py</span></code> to use them.</p></li>
|
||
<li><p>(2) <code class="docutils literal notranslate"><span class="pre">checkpoints-436000.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-438000.pt</span></code>, …, which are saved
|
||
every <code class="docutils literal notranslate"><span class="pre">--save-every-n</span></code> batches. You can pass <code class="docutils literal notranslate"><span class="pre">--iter</span></code> to
|
||
<code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/decode.py</span></code> to use them.</p></li>
|
||
</ul>
|
||
<p>We suggest that you try both types of checkpoints and choose the one
|
||
that produces the lowest WERs.</p>
|
||
</div></blockquote>
|
||
</div>
|
||
<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
|
||
$<span class="w"> </span>./pruned_transducer_stateless4/decode.py<span class="w"> </span>--help
|
||
</pre></div>
|
||
</div>
|
||
<p>shows the options for decoding.</p>
|
||
<p>The following shows two examples (for two types of checkpoints):</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="k">for</span><span class="w"> </span>m<span class="w"> </span><span class="k">in</span><span class="w"> </span>greedy_search<span class="w"> </span>fast_beam_search<span class="w"> </span>modified_beam_search<span class="p">;</span><span class="w"> </span><span class="k">do</span>
|
||
<span class="w"> </span><span class="k">for</span><span class="w"> </span>epoch<span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">25</span><span class="w"> </span><span class="m">20</span><span class="p">;</span><span class="w"> </span><span class="k">do</span>
|
||
<span class="w"> </span><span class="k">for</span><span class="w"> </span>avg<span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">7</span><span class="w"> </span><span class="m">5</span><span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="m">1</span><span class="p">;</span><span class="w"> </span><span class="k">do</span>
|
||
<span class="w"> </span>./pruned_transducer_stateless4/decode.py<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--epoch<span class="w"> </span><span class="nv">$epoch</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--avg<span class="w"> </span><span class="nv">$avg</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--exp-dir<span class="w"> </span>pruned_transducer_stateless4/exp<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">600</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--decoding-method<span class="w"> </span><span class="nv">$m</span>
|
||
<span class="w"> </span><span class="k">done</span>
|
||
<span class="w"> </span><span class="k">done</span>
|
||
<span class="k">done</span>
|
||
</pre></div>
|
||
</div>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="k">for</span><span class="w"> </span>m<span class="w"> </span><span class="k">in</span><span class="w"> </span>greedy_search<span class="w"> </span>fast_beam_search<span class="w"> </span>modified_beam_search<span class="p">;</span><span class="w"> </span><span class="k">do</span>
|
||
<span class="w"> </span><span class="k">for</span><span class="w"> </span>iter<span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">474000</span><span class="p">;</span><span class="w"> </span><span class="k">do</span>
|
||
<span class="w"> </span><span class="k">for</span><span class="w"> </span>avg<span class="w"> </span><span class="k">in</span><span class="w"> </span><span class="m">8</span><span class="w"> </span><span class="m">10</span><span class="w"> </span><span class="m">12</span><span class="w"> </span><span class="m">14</span><span class="w"> </span><span class="m">16</span><span class="w"> </span><span class="m">18</span><span class="p">;</span><span class="w"> </span><span class="k">do</span>
|
||
<span class="w"> </span>./pruned_transducer_stateless4/decode.py<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--iter<span class="w"> </span><span class="nv">$iter</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--avg<span class="w"> </span><span class="nv">$avg</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--exp-dir<span class="w"> </span>pruned_transducer_stateless4/exp<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">600</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--decoding-method<span class="w"> </span><span class="nv">$m</span>
|
||
<span class="w"> </span><span class="k">done</span>
|
||
<span class="w"> </span><span class="k">done</span>
|
||
<span class="k">done</span>
|
||
</pre></div>
|
||
</div>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>Supporting decoding methods are as follows:</p>
|
||
<blockquote>
|
||
<div><ul class="simple">
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">greedy_search</span></code> : It takes the symbol with largest posterior probability
|
||
of each frame as the decoding result.</p></li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">beam_search</span></code> : It implements Algorithm 1 in <a class="reference external" href="https://arxiv.org/pdf/1211.3711.pdf">https://arxiv.org/pdf/1211.3711.pdf</a> and
|
||
<a class="reference external" href="https://github.com/espnet/espnet/blob/master/espnet/nets/beam_search_transducer.py#L247">espnet/nets/beam_search_transducer.py</a>
|
||
is used as a reference. Basicly, it keeps topk states for each frame, and expands the kept states with their own contexts to
|
||
next frame.</p></li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">modified_beam_search</span></code> : It implements the same algorithm as <code class="docutils literal notranslate"><span class="pre">beam_search</span></code> above, but it
|
||
runs in batch mode with <code class="docutils literal notranslate"><span class="pre">--max-sym-per-frame=1</span></code> being hardcoded.</p></li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> : It implements graph composition between the output <code class="docutils literal notranslate"><span class="pre">log_probs</span></code> and
|
||
given <code class="docutils literal notranslate"><span class="pre">FSAs</span></code>. It is hard to describe the details in several lines of texts, you can read
|
||
our paper in <a class="reference external" href="https://arxiv.org/pdf/2211.00484.pdf">https://arxiv.org/pdf/2211.00484.pdf</a> or our <a class="reference external" href="https://github.com/k2-fsa/k2/blob/master/k2/csrc/rnnt_decode.h">rnnt decode code in k2</a>. <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> can decode with <code class="docutils literal notranslate"><span class="pre">FSAs</span></code> on GPU efficiently.</p></li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search_LG</span></code> : The same as <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> above, <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> uses
|
||
an trivial graph that has only one state, while <code class="docutils literal notranslate"><span class="pre">fast_beam_search_LG</span></code> uses an LG graph
|
||
(with N-gram LM).</p></li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search_nbest</span></code> : It produces the decoding results as follows:</p>
|
||
<ul>
|
||
<li><ol class="arabic simple">
|
||
<li><p>Use <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> to get a lattice</p></li>
|
||
</ol>
|
||
</li>
|
||
<li><ol class="arabic simple" start="2">
|
||
<li><p>Select <code class="docutils literal notranslate"><span class="pre">num_paths</span></code> paths from the lattice using <code class="docutils literal notranslate"><span class="pre">k2.random_paths()</span></code></p></li>
|
||
</ol>
|
||
</li>
|
||
<li><ol class="arabic simple" start="3">
|
||
<li><p>Unique the selected paths</p></li>
|
||
</ol>
|
||
</li>
|
||
<li><ol class="arabic simple" start="4">
|
||
<li><p>Intersect the selected paths with the lattice and compute the
|
||
shortest path from the intersection result</p></li>
|
||
</ol>
|
||
</li>
|
||
<li><ol class="arabic simple" start="5">
|
||
<li><p>The path with the largest score is used as the decoding output.</p></li>
|
||
</ol>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search_nbest_LG</span></code> : It implements same logic as <code class="docutils literal notranslate"><span class="pre">fast_beam_search_nbest</span></code>, the
|
||
only difference is that it uses <code class="docutils literal notranslate"><span class="pre">fast_beam_search_LG</span></code> to generate the lattice.</p></li>
|
||
</ul>
|
||
</div></blockquote>
|
||
</div>
|
||
</section>
|
||
<section id="export-model">
|
||
<h2>Export Model<a class="headerlink" href="#export-model" title="Permalink to this heading"></a></h2>
|
||
<p><a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless4/export.py">pruned_transducer_stateless4/export.py</a> supports exporting checkpoints from <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/exp</span></code> in the following ways.</p>
|
||
<section id="export-model-state-dict">
|
||
<h3>Export <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code><a class="headerlink" href="#export-model-state-dict" title="Permalink to this heading"></a></h3>
|
||
<p>Checkpoints saved by <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/train.py</span></code> also include
|
||
<code class="docutils literal notranslate"><span class="pre">optimizer.state_dict()</span></code>. It is useful for resuming training. But after training,
|
||
we are interested only in <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code>. You can use the following
|
||
command to extract <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code>.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Assume that --epoch 25 --avg 3 produces the smallest WER</span>
|
||
<span class="c1"># (You can get such information after running ./pruned_transducer_stateless4/decode.py)</span>
|
||
|
||
<span class="nv">epoch</span><span class="o">=</span><span class="m">25</span>
|
||
<span class="nv">avg</span><span class="o">=</span><span class="m">3</span>
|
||
|
||
./pruned_transducer_stateless4/export.py<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--exp-dir<span class="w"> </span>./pruned_transducer_stateless4/exp<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--bpe-model<span class="w"> </span>data/lang_bpe_500/bpe.model<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--epoch<span class="w"> </span><span class="nv">$epoch</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--avg<span class="w"> </span><span class="nv">$avg</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>It will generate a file <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/exp/pretrained.pt</span></code>.</p>
|
||
<div class="admonition hint">
|
||
<p class="admonition-title">Hint</p>
|
||
<p>To use the generated <code class="docutils literal notranslate"><span class="pre">pretrained.pt</span></code> for <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/decode.py</span></code>,
|
||
you can run:</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>pruned_transducer_stateless4/exp
|
||
ln<span class="w"> </span>-s<span class="w"> </span>pretrained.pt<span class="w"> </span>epoch-999.pt
|
||
</pre></div>
|
||
</div>
|
||
<p>And then pass <code class="docutils literal notranslate"><span class="pre">--epoch</span> <span class="pre">999</span> <span class="pre">--avg</span> <span class="pre">1</span> <span class="pre">--use-averaged-model</span> <span class="pre">0</span></code> to
|
||
<code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/decode.py</span></code>.</p>
|
||
</div>
|
||
<p>To use the exported model with <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/pretrained.py</span></code>, you
|
||
can run:</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./pruned_transducer_stateless4/pretrained.py<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--checkpoint<span class="w"> </span>./pruned_transducer_stateless4/exp/pretrained.pt<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--bpe-model<span class="w"> </span>./data/lang_bpe_500/bpe.model<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--method<span class="w"> </span>greedy_search<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>/path/to/foo.wav<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>/path/to/bar.wav
|
||
</pre></div>
|
||
</div>
|
||
</section>
|
||
<section id="export-model-using-torch-jit-script">
|
||
<h3>Export model using <code class="docutils literal notranslate"><span class="pre">torch.jit.script()</span></code><a class="headerlink" href="#export-model-using-torch-jit-script" title="Permalink to this heading"></a></h3>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./pruned_transducer_stateless4/export.py<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--exp-dir<span class="w"> </span>./pruned_transducer_stateless4/exp<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--bpe-model<span class="w"> </span>data/lang_bpe_500/bpe.model<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--epoch<span class="w"> </span><span class="m">25</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--avg<span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--jit<span class="w"> </span><span class="m">1</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>It will generate a file <code class="docutils literal notranslate"><span class="pre">cpu_jit.pt</span></code> in the given <code class="docutils literal notranslate"><span class="pre">exp_dir</span></code>. You can later
|
||
load it by <code class="docutils literal notranslate"><span class="pre">torch.jit.load("cpu_jit.pt")</span></code>.</p>
|
||
<p>Note <code class="docutils literal notranslate"><span class="pre">cpu</span></code> in the name <code class="docutils literal notranslate"><span class="pre">cpu_jit.pt</span></code> means the parameters when loaded into Python
|
||
are on CPU. You can use <code class="docutils literal notranslate"><span class="pre">to("cuda")</span></code> to move them to a CUDA device.</p>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>You will need this <code class="docutils literal notranslate"><span class="pre">cpu_jit.pt</span></code> when deploying with Sherpa framework.</p>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<section id="download-pretrained-models">
|
||
<h2>Download pretrained models<a class="headerlink" href="#download-pretrained-models" title="Permalink to this heading"></a></h2>
|
||
<p>If you don’t want to train from scratch, you can download the pretrained models
|
||
by visiting the following links:</p>
|
||
<blockquote>
|
||
<div><ul class="simple">
|
||
<li><p><a class="reference external" href="https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12">pruned_transducer_stateless</a></p></li>
|
||
<li><p><a class="reference external" href="https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless2-2022-04-29">pruned_transducer_stateless2</a></p></li>
|
||
<li><p><a class="reference external" href="https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless4-2022-06-03">pruned_transducer_stateless4</a></p></li>
|
||
<li><p><a class="reference external" href="https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-2022-07-07">pruned_transducer_stateless5</a></p></li>
|
||
</ul>
|
||
<p>See <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md">https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md</a>
|
||
for the details of the above pretrained models</p>
|
||
</div></blockquote>
|
||
</section>
|
||
<section id="deploy-with-sherpa">
|
||
<h2>Deploy with Sherpa<a class="headerlink" href="#deploy-with-sherpa" title="Permalink to this heading"></a></h2>
|
||
<p>Please see <a class="reference external" href="https://k2-fsa.github.io/sherpa/python/offline_asr/conformer/librispeech.html#">https://k2-fsa.github.io/sherpa/python/offline_asr/conformer/librispeech.html#</a>
|
||
for how to deploy the models in <code class="docutils literal notranslate"><span class="pre">sherpa</span></code>.</p>
|
||
</section>
|
||
</section>
|
||
|
||
|
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