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<section id="decoding-with-language-models">
<h1>Decoding with language models<a class="headerlink" href="#decoding-with-language-models" title="Permalink to this heading"></a></h1>
<p>This section describes how to use external langugage models
during decoding to improve the WER of transducer models. To train an external language model,
please refer to this tutorial: <a class="reference internal" href="../recipes/RNN-LM/librispeech/lm-training.html#train-nnlm"><span class="std std-ref">Train an RNN language model</span></a>.</p>
<p>The following decoding methods with external langugage models are available:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 33.3%" />
<col style="width: 66.7%" />
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<thead>
<tr class="row-odd"><th class="head"><p>Decoding method</p></th>
<th class="head"><p>beam=4</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">modified_beam_search</span></code></p></td>
<td><p>Beam search (i.e. really n-best decoding, the “beam” is the value of n), similar to the original RNN-T paper. Note, this method does not use language model.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">modified_beam_search_lm_shallow_fusion</span></code></p></td>
<td><p>As <code class="docutils literal notranslate"><span class="pre">modified_beam_search</span></code>, but interpolate RNN-T scores with language model scores, also known as shallow fusion</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">modified_beam_search_LODR</span></code></p></td>
<td><p>As <code class="docutils literal notranslate"><span class="pre">modified_beam_search_lm_shallow_fusion</span></code>, but subtract score of a (BPE-symbol-level) bigram backoff language model used as an approximation to the internal language model of RNN-T.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">modified_beam_search_lm_rescore</span></code></p></td>
<td><p>As <code class="docutils literal notranslate"><span class="pre">modified_beam_search</span></code>, but rescore the n-best hypotheses with external language model (e.g. RNNLM) and re-rank them.</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">modified_beam_search_lm_rescore_LODR</span></code></p></td>
<td><p>As <code class="docutils literal notranslate"><span class="pre">modified_beam_search_lm_rescore</span></code>, but also subtract the score of a (BPE-symbol-level) bigram backoff language model during re-ranking.</p></td>
</tr>
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