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<section id="lm-rescoring-for-transducer">
<span id="rescoring"></span><h1>LM rescoring for Transducer<a class="headerlink" href="#lm-rescoring-for-transducer" title="Permalink to this heading"></a></h1>
<p>LM rescoring is a commonly used approach to incorporate external LM information. Unlike shallow-fusion-based
methods (see <span class="xref std std-ref">shallow-fusion</span>, <a class="reference internal" href="LODR.html#lodr"><span class="std std-ref">LODR for RNN Transducer</span></a>), rescoring is usually performed to re-rank the n-best hypotheses after beam search.
Rescoring is usually more efficient than shallow fusion since less computation is performed on the external LM.
In this tutorial, we will show you how to use external LM to rescore the n-best hypotheses decoded from neural transducer models in
<a class="reference external" href="https://github.com/k2-fsa/icefall">icefall</a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This tutorial is based on the recipe
<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless7_streaming">pruned_transducer_stateless7_streaming</a>,
which is a streaming transducer model trained on <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a>.
However, you can easily apply shallow fusion to other recipes.
If you encounter any problems, please open an issue <a class="reference external" href="https://github.com/k2-fsa/icefall/issues">here</a>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>For simplicity, the training and testing corpus in this tutorial is the same (<a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a>). However, you can change the testing set
to any other domains (e.g <a class="reference external" href="https://github.com/SpeechColab/GigaSpeech">GigaSpeech</a>) and use an external LM trained on that domain.</p>
</div>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>We recommend you to use a GPU for decoding.</p>
</div>
<p>For illustration purpose, we will use a pre-trained ASR model from this <a class="reference external" href="https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29">link</a>.
If you want to train your model from scratch, please have a look at <a class="reference internal" href="../recipes/Non-streaming-ASR/librispeech/pruned_transducer_stateless.html#non-streaming-librispeech-pruned-transducer-stateless"><span class="std std-ref">Pruned transducer statelessX</span></a>.</p>
<p>As the initial step, let’s download the pre-trained model.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">GIT_LFS_SKIP_SMUDGE</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
$<span class="w"> </span><span class="nb">pushd</span><span class="w"> </span>icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$<span class="w"> </span>git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span><span class="s2">&quot;pretrained.pt&quot;</span>
$<span class="w"> </span>ln<span class="w"> </span>-s<span class="w"> </span>pretrained.pt<span class="w"> </span>epoch-99.pt<span class="w"> </span><span class="c1"># create a symbolic link so that the checkpoint can be loaded</span>
</pre></div>
</div>
<p>As usual, we first test the model’s performance without external LM. This can be done via the following command:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">exp_dir</span><span class="o">=</span>./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/
$<span class="w"> </span>./pruned_transducer_stateless7_streaming/decode.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--epoch<span class="w"> </span><span class="m">99</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--avg<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-averaged-model<span class="w"> </span>False<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--exp-dir<span class="w"> </span><span class="nv">$exp_dir</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--bpe-model<span class="w"> </span>./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model
<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>--decode-chunk-len<span class="w"> </span><span class="m">32</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--decoding-method<span class="w"> </span>modified_beam_search
</pre></div>
</div>
<p>The following WERs are achieved on test-clean and test-other:</p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>$ For test-clean, WER of different settings are:
$ beam_size_4 3.11 best for test-clean
$ For test-other, WER of different settings are:
$ beam_size_4 7.93 best for test-other
</pre></div>
</div>
<p>Now, we will try to improve the above WER numbers via external LM rescoring. We will download
a pre-trained LM from this <a class="reference external" href="https://huggingface.co/ezerhouni/icefall-librispeech-rnn-lm">link</a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This is an RNN LM trained on the LibriSpeech text corpus. So it might not be ideal for other corpus.
You may also train a RNN LM from scratch. Please refer to this <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/icefall/rnn_lm/train.py">script</a>
for training a RNN LM and this <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/icefall/transformer_lm/train.py">script</a> to train a transformer LM.</p>
</div>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="c1"># download the external LM</span>
$<span class="w"> </span><span class="nv">GIT_LFS_SKIP_SMUDGE</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>https://huggingface.co/ezerhouni/icefall-librispeech-rnn-lm
$<span class="w"> </span><span class="c1"># create a symbolic link so that the checkpoint can be loaded</span>
$<span class="w"> </span><span class="nb">pushd</span><span class="w"> </span>icefall-librispeech-rnn-lm/exp
$<span class="w"> </span>git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span><span class="s2">&quot;pretrained.pt&quot;</span>
$<span class="w"> </span>ln<span class="w"> </span>-s<span class="w"> </span>pretrained.pt<span class="w"> </span>epoch-99.pt
$<span class="w"> </span><span class="nb">popd</span>
</pre></div>
</div>
<p>With the RNNLM available, we can rescore the n-best hypotheses generated from <cite>modified_beam_search</cite>. Here,
<cite>n</cite> should be the number of beams, i.e <code class="docutils literal notranslate"><span class="pre">--beam-size</span></code>. The command for LM rescoring is
as follows. Note that the <code class="docutils literal notranslate"><span class="pre">--decoding-method</span></code> is set to <cite>modified_beam_search_lm_rescore</cite> and <code class="docutils literal notranslate"><span class="pre">--use-shallow-fusion</span></code>
is set to <cite>False</cite>.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">exp_dir</span><span class="o">=</span>./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$<span class="w"> </span><span class="nv">lm_dir</span><span class="o">=</span>./icefall-librispeech-rnn-lm/exp
$<span class="w"> </span><span class="nv">lm_scale</span><span class="o">=</span><span class="m">0</span>.43
$<span class="w"> </span>./pruned_transducer_stateless7_streaming/decode.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--epoch<span class="w"> </span><span class="m">99</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--avg<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-averaged-model<span class="w"> </span>False<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--beam-size<span class="w"> </span><span class="m">4</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--exp-dir<span class="w"> </span><span class="nv">$exp_dir</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">600</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--decode-chunk-len<span class="w"> </span><span class="m">32</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--decoding-method<span class="w"> </span>modified_beam_search_lm_rescore<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--bpe-model<span class="w"> </span>./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model
<span class="w"> </span>--use-shallow-fusion<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-type<span class="w"> </span>rnn<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-exp-dir<span class="w"> </span><span class="nv">$lm_dir</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-epoch<span class="w"> </span><span class="m">99</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-scale<span class="w"> </span><span class="nv">$lm_scale</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-avg<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--rnn-lm-embedding-dim<span class="w"> </span><span class="m">2048</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--rnn-lm-hidden-dim<span class="w"> </span><span class="m">2048</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--rnn-lm-num-layers<span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-vocab-size<span class="w"> </span><span class="m">500</span>
</pre></div>
</div>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>$ For test-clean, WER of different settings are:
$ beam_size_4 2.93 best for test-clean
$ For test-other, WER of different settings are:
$ beam_size_4 7.6 best for test-other
</pre></div>
</div>
<p>Great! We made some improvements! Increasing the size of the n-best hypotheses will further boost the performance,
see the following table:</p>
<table class="docutils align-default" id="id1">
<caption><span class="caption-number">Table 3 </span><span class="caption-text">WERs of LM rescoring with different beam sizes</span><a class="headerlink" href="#id1" title="Permalink to this table"></a></caption>
<colgroup>
<col style="width: 33%" />
<col style="width: 33%" />
<col style="width: 33%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Beam size</p></th>
<th class="head"><p>test-clean</p></th>
<th class="head"><p>test-other</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>4</p></td>
<td><p>2.93</p></td>
<td><p>7.6</p></td>
</tr>
<tr class="row-odd"><td><p>8</p></td>
<td><p>2.67</p></td>
<td><p>7.11</p></td>
</tr>
<tr class="row-even"><td><p>12</p></td>
<td><p>2.59</p></td>
<td><p>6.86</p></td>
</tr>
</tbody>
</table>
<p>In fact, we can also apply LODR (see <a class="reference internal" href="LODR.html#lodr"><span class="std std-ref">LODR for RNN Transducer</span></a>) when doing LM rescoring. To do so, we need to
download the bi-gram required by LODR:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="c1"># download the bi-gram</span>
$<span class="w"> </span>git<span class="w"> </span>lfs<span class="w"> </span>install
$<span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>https://huggingface.co/marcoyang/librispeech_bigram
$<span class="w"> </span><span class="nb">pushd</span><span class="w"> </span>data/lang_bpe_500
$<span class="w"> </span>ln<span class="w"> </span>-s<span class="w"> </span>../../librispeech_bigram/2gram.arpa<span class="w"> </span>.
$<span class="w"> </span><span class="nb">popd</span>
</pre></div>
</div>
<p>Then we can performn LM rescoring + LODR by changing the decoding method to <cite>modified_beam_search_lm_rescore_LODR</cite>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This decoding method requires the dependency of <a class="reference external" href="https://github.com/kpu/kenlm">kenlm</a>. You can install it
via this command: <cite>pip install https://github.com/kpu/kenlm/archive/master.zip</cite>.</p>
</div>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">exp_dir</span><span class="o">=</span>./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$<span class="w"> </span><span class="nv">lm_dir</span><span class="o">=</span>./icefall-librispeech-rnn-lm/exp
$<span class="w"> </span><span class="nv">lm_scale</span><span class="o">=</span><span class="m">0</span>.43
$<span class="w"> </span>./pruned_transducer_stateless7_streaming/decode.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--epoch<span class="w"> </span><span class="m">99</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--avg<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-averaged-model<span class="w"> </span>False<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--beam-size<span class="w"> </span><span class="m">4</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--exp-dir<span class="w"> </span><span class="nv">$exp_dir</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">600</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--decode-chunk-len<span class="w"> </span><span class="m">32</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--decoding-method<span class="w"> </span>modified_beam_search_lm_rescore_LODR<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--bpe-model<span class="w"> </span>./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model
<span class="w"> </span>--use-shallow-fusion<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-type<span class="w"> </span>rnn<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-exp-dir<span class="w"> </span><span class="nv">$lm_dir</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-epoch<span class="w"> </span><span class="m">99</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-scale<span class="w"> </span><span class="nv">$lm_scale</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-avg<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--rnn-lm-embedding-dim<span class="w"> </span><span class="m">2048</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--rnn-lm-hidden-dim<span class="w"> </span><span class="m">2048</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--rnn-lm-num-layers<span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--lm-vocab-size<span class="w"> </span><span class="m">500</span>
</pre></div>
</div>
<p>You should see the following WERs after executing the commands above:</p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>$ For test-clean, WER of different settings are:
$ beam_size_4 2.9 best for test-clean
$ For test-other, WER of different settings are:
$ beam_size_4 7.57 best for test-other
</pre></div>
</div>
<p>It’s slightly better than LM rescoring. If we further increase the beam size, we will see
further improvements from LM rescoring + LODR:</p>
<table class="docutils align-default" id="id2">
<caption><span class="caption-number">Table 4 </span><span class="caption-text">WERs of LM rescoring + LODR with different beam sizes</span><a class="headerlink" href="#id2" title="Permalink to this table"></a></caption>
<colgroup>
<col style="width: 33%" />
<col style="width: 33%" />
<col style="width: 33%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Beam size</p></th>
<th class="head"><p>test-clean</p></th>
<th class="head"><p>test-other</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>4</p></td>
<td><p>2.9</p></td>
<td><p>7.57</p></td>
</tr>
<tr class="row-odd"><td><p>8</p></td>
<td><p>2.63</p></td>
<td><p>7.04</p></td>
</tr>
<tr class="row-even"><td><p>12</p></td>
<td><p>2.52</p></td>
<td><p>6.73</p></td>
</tr>
</tbody>
</table>
<p>As mentioned earlier, LM rescoring is usually faster than shallow-fusion based methods.
Here, we benchmark the WERs and decoding speed of them:</p>
<table class="docutils align-default" id="id3">
<caption><span class="caption-number">Table 5 </span><span class="caption-text">LM-rescoring-based methods vs shallow-fusion-based methods (The numbers in each field is WER on test-clean, WER on test-other and decoding time on test-clean)</span><a class="headerlink" href="#id3" title="Permalink to this table"></a></caption>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Decoding method</p></th>
<th class="head"><p>beam=4</p></th>
<th class="head"><p>beam=8</p></th>
<th class="head"><p>beam=12</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><cite>modified_beam_search</cite></p></td>
<td><p>3.11/7.93; 132s</p></td>
<td><p>3.1/7.95; 177s</p></td>
<td><p>3.1/7.96; 210s</p></td>
</tr>
<tr class="row-odd"><td><p><cite>modified_beam_search_lm_shallow_fusion</cite></p></td>
<td><p>2.77/7.08; 262s</p></td>
<td><p>2.62/6.65; 352s</p></td>
<td><p>2.58/6.65; 488s</p></td>
</tr>
<tr class="row-even"><td><p>LODR</p></td>
<td><p>2.61/6.74; 400s</p></td>
<td><p>2.45/6.38; 610s</p></td>
<td><p>2.4/6.23; 870s</p></td>
</tr>
<tr class="row-odd"><td><p><cite>modified_beam_search_lm_rescore</cite></p></td>
<td><p>2.93/7.6; 156s</p></td>
<td><p>2.67/7.11; 203s</p></td>
<td><p>2.59/6.86; 255s</p></td>
</tr>
<tr class="row-even"><td><p><cite>modified_beam_search_lm_rescore_LODR</cite></p></td>
<td><p>2.9/7.57; 160s</p></td>
<td><p>2.63/7.04; 203s</p></td>
<td><p>2.52/6.73; 263s</p></td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Decoding is performed with a single 32G V100, we set <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code> to 600.
Decoding time here is only for reference and it may vary.</p>
</div>
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