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<section id="tdnn-lstm-ctc">
<h1>TDNN-LSTM-CTC<a class="headerlink" href="#tdnn-lstm-ctc" title="Permalink to this heading"></a></h1>
<p>This tutorial shows you how to run a TDNN-LSTM-CTC model with the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a> dataset.</p>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<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
the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
</div>
<section id="data-preparation">
<h2>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this heading"></a></h2>
<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
$<span class="w"> </span>./prepare.sh
</pre></div>
</div>
<p>The script <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> handles the data preparation for you, <strong>automagically</strong>.
All you need to do is to run it.</p>
<p>The data preparation contains several stages, you can use the following two
options:</p>
<blockquote>
<div><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">--stage</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--stop-stage</span></code></p></li>
</ul>
</div></blockquote>
<p>to control which stage(s) should be run. By default, all stages are executed.</p>
<p>For example,</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
$<span class="w"> </span>./prepare.sh<span class="w"> </span>--stage<span class="w"> </span><span class="m">0</span><span class="w"> </span>--stop-stage<span class="w"> </span><span class="m">0</span>
</pre></div>
</div>
<p>means to run only stage 0.</p>
<p>To run stage 2 to stage 5, use:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<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>
</pre></div>
</div>
<p>We provide the following YouTube video showing how to run <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>To get the latest news of <a class="reference external" href="https://github.com/k2-fsa">next-gen Kaldi</a>, please subscribe
the following YouTube channel by <a class="reference external" href="https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw">Nadira Povey</a>:</p>
<blockquote>
<div><p><a class="reference external" href="https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw">https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw</a></p>
</div></blockquote>
</div>
<div class="video_wrapper" style="">
<iframe allowfullscreen="true" src="https://www.youtube.com/embed/ofEIoJL-mGM" style="border: 0; height: 345px; width: 560px">
</iframe></div></section>
<section id="training">
<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading"></a></h2>
<p>Now describing the training of TDNN-LSTM-CTC model, contained in
the <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/tdnn_lstm_ctc">tdnn_lstm_ctc</a>
folder.</p>
<p>The command to run the training part is:</p>
<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">&quot;0,1,2,3&quot;</span>
$<span class="w"> </span>./tdnn_lstm_ctc/train.py<span class="w"> </span>--world-size<span class="w"> </span><span class="m">4</span>
</pre></div>
</div>
<p>By default, it will run <code class="docutils literal notranslate"><span class="pre">20</span></code> epochs. Training logs and checkpoints are saved
in <code class="docutils literal notranslate"><span class="pre">tdnn_lstm_ctc/exp</span></code>.</p>
<p>In <code class="docutils literal notranslate"><span class="pre">tdnn_lstm_ctc/exp</span></code>, you will find the following files:</p>
<blockquote>
<div><ul>
<li><p><code class="docutils literal notranslate"><span class="pre">epoch-0.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, …, <code class="docutils literal notranslate"><span class="pre">epoch-19.pt</span></code></p>
<p>These are checkpoint files, containing model <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and optimizer <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.
To resume training from some checkpoint, say <code class="docutils literal notranslate"><span class="pre">epoch-10.pt</span></code>, you can use:</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>./tdnn_lstm_ctc/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">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>tdnn_lstm_ctc/exp/tensorboard
$<span class="w"> </span>tensorboard<span class="w"> </span>dev<span class="w"> </span>upload<span class="w"> </span>--logdir<span class="w"> </span>.<span class="w"> </span>--description<span class="w"> </span><span class="s2">&quot;TDNN LSTM training for librispeech with icefall&quot;</span>
</pre></div>
</div>
</div></blockquote>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">log/log-train-xxxx</span></code></p>
<p>It is the detailed training log in text format, same as the one
you saw printed to the console during training.</p>
</li>
</ul>
</div></blockquote>
<p>To see available training options, you can use:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>./tdnn_lstm_ctc/train.py<span class="w"> </span>--help
</pre></div>
</div>
<p>Other training options, e.g., learning rate, results dir, etc., are
pre-configured in 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/tdnn_lstm_ctc/train.py">tdnn_lstm_ctc/train.py</a>.
Normally, you don’t need to change them. You can change them by modifying the code, if
you want.</p>
</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>
<p>The command for decoding is:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<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">&quot;0&quot;</span>
$<span class="w"> </span>./tdnn_lstm_ctc/decode.py
</pre></div>
</div>
<p>You will see the WER in the output log.</p>
<p>Decoded results are saved in <code class="docutils literal notranslate"><span class="pre">tdnn_lstm_ctc/exp</span></code>.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>./tdnn_lstm_ctc/decode.py<span class="w"> </span>--help
</pre></div>
</div>
<p>shows you the available decoding options.</p>
<p>Some commonly used options are:</p>
<blockquote>
<div><ul>
<li><p><code class="docutils literal notranslate"><span class="pre">--epoch</span></code></p>
<p>You can select which checkpoint to be used for decoding.
For instance, <code class="docutils literal notranslate"><span class="pre">./tdnn_lstm_ctc/decode.py</span> <span class="pre">--epoch</span> <span class="pre">10</span></code> means to use
<code class="docutils literal notranslate"><span class="pre">./tdnn_lstm_ctc/exp/epoch-10.pt</span></code> for decoding.</p>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">--avg</span></code></p>
<p>It’s related to model averaging. It specifies number of checkpoints
to be averaged. The averaged model is used for decoding.
For example, the following command:</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>./tdnn_lstm_ctc/decode.py<span class="w"> </span>--epoch<span class="w"> </span><span class="m">10</span><span class="w"> </span>--avg<span class="w"> </span><span class="m">3</span>
</pre></div>
</div>
</div></blockquote>
<p>uses the average of <code class="docutils literal notranslate"><span class="pre">epoch-8.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-9.pt</span></code> and <code class="docutils literal notranslate"><span class="pre">epoch-10.pt</span></code>
for decoding.</p>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">--export</span></code></p>
<p>If it is <code class="docutils literal notranslate"><span class="pre">True</span></code>, i.e., <code class="docutils literal notranslate"><span class="pre">./tdnn_lstm_ctc/decode.py</span> <span class="pre">--export</span> <span class="pre">1</span></code>, the code
will save the averaged model to <code class="docutils literal notranslate"><span class="pre">tdnn_lstm_ctc/exp/pretrained.pt</span></code>.
See <a class="reference internal" href="../timit/tdnn_lstm_ctc.html#tdnn-lstm-ctc-use-a-pre-trained-model"><span class="std std-ref">Pre-trained Model</span></a> for how to use it.</p>
</li>
</ul>
</div></blockquote>
</section>
<section id="pre-trained-model">
<span id="tdnn-lstm-ctc-use-a-pre-trained-model"></span><h2>Pre-trained Model<a class="headerlink" href="#pre-trained-model" title="Permalink to this heading"></a></h2>
<p>We have uploaded the pre-trained model to
<a class="reference external" href="https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc">https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc</a>.</p>
<p>The following shows you how to use the pre-trained model.</p>
<section id="install-kaldifeat">
<h3>Install kaldifeat<a class="headerlink" href="#install-kaldifeat" title="Permalink to this heading"></a></h3>
<p><a class="reference external" href="https://github.com/csukuangfj/kaldifeat">kaldifeat</a> is used to
extract features for a single sound file or multiple sound files
at the same time.</p>
<p>Please refer to <a class="reference external" href="https://github.com/csukuangfj/kaldifeat">https://github.com/csukuangfj/kaldifeat</a> for installation.</p>
</section>
<section id="download-the-pre-trained-model">
<h3>Download the pre-trained model<a class="headerlink" href="#download-the-pre-trained-model" title="Permalink to this heading"></a></h3>
<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>mkdir<span class="w"> </span>tmp
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>tmp
$<span class="w"> </span>git<span class="w"> </span>lfs<span class="w"> </span>install
$<span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc
</pre></div>
</div>
<div class="admonition caution">
<p class="admonition-title">Caution</p>
<p>You have to use <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">lfs</span></code> to download the pre-trained model.</p>
</div>
<div class="admonition caution">
<p class="admonition-title">Caution</p>
<p>In order to use this pre-trained model, your k2 version has to be v1.7 or later.</p>
</div>
<p>After downloading, you will have the following files:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
$<span class="w"> </span>tree<span class="w"> </span>tmp
</pre></div>
</div>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>tmp/
<span class="sb">`</span>--<span class="w"> </span>icefall_asr_librispeech_tdnn-lstm_ctc
<span class="w"> </span><span class="p">|</span>--<span class="w"> </span>README.md
<span class="w"> </span><span class="p">|</span>--<span class="w"> </span>data
<span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>lang_phone
<span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>HLG.pt
<span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>tokens.txt
<span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="sb">`</span>--<span class="w"> </span>words.txt
<span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="sb">`</span>--<span class="w"> </span>lm
<span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="sb">`</span>--<span class="w"> </span>G_4_gram.pt
<span class="w"> </span><span class="p">|</span>--<span class="w"> </span>exp
<span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="sb">`</span>--<span class="w"> </span>pretrained.pt
<span class="w"> </span><span class="sb">`</span>--<span class="w"> </span>test_wavs
<span class="w"> </span><span class="p">|</span>--<span class="w"> </span><span class="m">1089</span>-134686-0001.flac
<span class="w"> </span><span class="p">|</span>--<span class="w"> </span><span class="m">1221</span>-135766-0001.flac
<span class="w"> </span><span class="p">|</span>--<span class="w"> </span><span class="m">1221</span>-135766-0002.flac
<span class="w"> </span><span class="sb">`</span>--<span class="w"> </span>trans.txt
<span class="m">6</span><span class="w"> </span>directories,<span class="w"> </span><span class="m">10</span><span class="w"> </span>files
</pre></div>
</div>
<p><strong>File descriptions</strong>:</p>
<blockquote>
<div><ul>
<li><p><code class="docutils literal notranslate"><span class="pre">data/lang_phone/HLG.pt</span></code></p>
<blockquote>
<div><p>It is the decoding graph.</p>
</div></blockquote>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">data/lang_phone/tokens.txt</span></code></p>
<blockquote>
<div><p>It contains tokens and their IDs.</p>
</div></blockquote>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">data/lang_phone/words.txt</span></code></p>
<blockquote>
<div><p>It contains words and their IDs.</p>
</div></blockquote>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">data/lm/G_4_gram.pt</span></code></p>
<blockquote>
<div><p>It is a 4-gram LM, useful for LM rescoring.</p>
</div></blockquote>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">exp/pretrained.pt</span></code></p>
<blockquote>
<div><p>It contains pre-trained model parameters, obtained by averaging
checkpoints from <code class="docutils literal notranslate"><span class="pre">epoch-14.pt</span></code> to <code class="docutils literal notranslate"><span class="pre">epoch-19.pt</span></code>.
Note: We have removed optimizer <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> to reduce file size.</p>
</div></blockquote>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">test_waves/*.flac</span></code></p>
<blockquote>
<div><p>It contains some test sound files from LibriSpeech <code class="docutils literal notranslate"><span class="pre">test-clean</span></code> dataset.</p>
</div></blockquote>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">test_waves/trans.txt</span></code></p>
<blockquote>
<div><p>It contains the reference transcripts for the sound files in <code class="docutils literal notranslate"><span class="pre">test_waves/</span></code>.</p>
</div></blockquote>
</li>
</ul>
</div></blockquote>
<p>The information of the test sound files is listed below:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>soxi<span class="w"> </span>tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/*.flac
Input<span class="w"> </span>File<span class="w"> </span>:<span class="w"> </span><span class="s1">&#39;tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac&#39;</span>
Channels<span class="w"> </span>:<span class="w"> </span><span class="m">1</span>
Sample<span class="w"> </span>Rate<span class="w"> </span>:<span class="w"> </span><span class="m">16000</span>
Precision<span class="w"> </span>:<span class="w"> </span><span class="m">16</span>-bit
Duration<span class="w"> </span>:<span class="w"> </span><span class="m">00</span>:00:06.62<span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">106000</span><span class="w"> </span>samples<span class="w"> </span>~<span class="w"> </span><span class="m">496</span>.875<span class="w"> </span>CDDA<span class="w"> </span>sectors
File<span class="w"> </span>Size<span class="w"> </span>:<span class="w"> </span>116k
Bit<span class="w"> </span>Rate<span class="w"> </span>:<span class="w"> </span>140k
Sample<span class="w"> </span>Encoding:<span class="w"> </span><span class="m">16</span>-bit<span class="w"> </span>FLAC
Input<span class="w"> </span>File<span class="w"> </span>:<span class="w"> </span><span class="s1">&#39;tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac&#39;</span>
Channels<span class="w"> </span>:<span class="w"> </span><span class="m">1</span>
Sample<span class="w"> </span>Rate<span class="w"> </span>:<span class="w"> </span><span class="m">16000</span>
Precision<span class="w"> </span>:<span class="w"> </span><span class="m">16</span>-bit
Duration<span class="w"> </span>:<span class="w"> </span><span class="m">00</span>:00:16.71<span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">267440</span><span class="w"> </span>samples<span class="w"> </span>~<span class="w"> </span><span class="m">1253</span>.62<span class="w"> </span>CDDA<span class="w"> </span>sectors
File<span class="w"> </span>Size<span class="w"> </span>:<span class="w"> </span>343k
Bit<span class="w"> </span>Rate<span class="w"> </span>:<span class="w"> </span>164k
Sample<span class="w"> </span>Encoding:<span class="w"> </span><span class="m">16</span>-bit<span class="w"> </span>FLAC
Input<span class="w"> </span>File<span class="w"> </span>:<span class="w"> </span><span class="s1">&#39;tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac&#39;</span>
Channels<span class="w"> </span>:<span class="w"> </span><span class="m">1</span>
Sample<span class="w"> </span>Rate<span class="w"> </span>:<span class="w"> </span><span class="m">16000</span>
Precision<span class="w"> </span>:<span class="w"> </span><span class="m">16</span>-bit
Duration<span class="w"> </span>:<span class="w"> </span><span class="m">00</span>:00:04.83<span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">77200</span><span class="w"> </span>samples<span class="w"> </span>~<span class="w"> </span><span class="m">361</span>.875<span class="w"> </span>CDDA<span class="w"> </span>sectors
File<span class="w"> </span>Size<span class="w"> </span>:<span class="w"> </span>105k
Bit<span class="w"> </span>Rate<span class="w"> </span>:<span class="w"> </span>174k
Sample<span class="w"> </span>Encoding:<span class="w"> </span><span class="m">16</span>-bit<span class="w"> </span>FLAC
Total<span class="w"> </span>Duration<span class="w"> </span>of<span class="w"> </span><span class="m">3</span><span class="w"> </span>files:<span class="w"> </span><span class="m">00</span>:00:28.16
</pre></div>
</div>
</section>
<section id="inference-with-a-pre-trained-model">
<h3>Inference with a pre-trained model<a class="headerlink" href="#inference-with-a-pre-trained-model" title="Permalink to this heading"></a></h3>
<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>./tdnn_lstm_ctc/pretrained.py<span class="w"> </span>--help
</pre></div>
</div>
<p>shows the usage information of <code class="docutils literal notranslate"><span class="pre">./tdnn_lstm_ctc/pretrained.py</span></code>.</p>
<p>To decode with <code class="docutils literal notranslate"><span class="pre">1best</span></code> method, we can use:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./tdnn_lstm_ctc/pretrained.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--checkpoint<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--words-file<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--HLG<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac
</pre></div>
</div>
<p>The output is:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">57</span><span class="p">:</span><span class="mi">13</span><span class="p">,</span><span class="mi">315</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">168</span><span class="p">]</span> <span class="n">device</span><span class="p">:</span> <span class="n">cuda</span><span class="p">:</span><span class="mi">0</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">57</span><span class="p">:</span><span class="mi">13</span><span class="p">,</span><span class="mi">315</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">170</span><span class="p">]</span> <span class="n">Creating</span> <span class="n">model</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">57</span><span class="p">:</span><span class="mi">18</span><span class="p">,</span><span class="mi">331</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">182</span><span class="p">]</span> <span class="n">Loading</span> <span class="n">HLG</span> <span class="kn">from</span> <span class="nn">.</span><span class="o">/</span><span class="n">tmp</span><span class="o">/</span><span class="n">icefall_asr_librispeech_tdnn</span><span class="o">-</span><span class="n">lstm_ctc</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">lang_phone</span><span class="o">/</span><span class="n">HLG</span><span class="o">.</span><span class="n">pt</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">57</span><span class="p">:</span><span class="mi">27</span><span class="p">,</span><span class="mi">581</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">199</span><span class="p">]</span> <span class="n">Constructing</span> <span class="n">Fbank</span> <span class="n">computer</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">57</span><span class="p">:</span><span class="mi">27</span><span class="p">,</span><span class="mi">584</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">209</span><span class="p">]</span> <span class="n">Reading</span> <span class="n">sound</span> <span class="n">files</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac&#39;</span><span class="p">,</span> <span class="s1">&#39;./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac&#39;</span><span class="p">,</span> <span class="s1">&#39;./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac&#39;</span><span class="p">]</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">57</span><span class="p">:</span><span class="mi">27</span><span class="p">,</span><span class="mi">599</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">215</span><span class="p">]</span> <span class="n">Decoding</span> <span class="n">started</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">57</span><span class="p">:</span><span class="mi">27</span><span class="p">,</span><span class="mi">791</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">245</span><span class="p">]</span> <span class="n">Use</span> <span class="n">HLG</span> <span class="n">decoding</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">57</span><span class="p">:</span><span class="mi">28</span><span class="p">,</span><span class="mi">098</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">266</span><span class="p">]</span>
<span class="o">./</span><span class="n">tmp</span><span class="o">/</span><span class="n">icefall_asr_librispeech_tdnn</span><span class="o">-</span><span class="n">lstm_ctc</span><span class="o">/</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1089</span><span class="o">-</span><span class="mi">134686</span><span class="o">-</span><span class="mf">0001.</span><span class="n">flac</span><span class="p">:</span>
<span class="n">AFTER</span> <span class="n">EARLY</span> <span class="n">NIGHTFALL</span> <span class="n">THE</span> <span class="n">YELLOW</span> <span class="n">LAMPS</span> <span class="n">WOULD</span> <span class="n">LIGHT</span> <span class="n">UP</span> <span class="n">HERE</span> <span class="n">AND</span> <span class="n">THERE</span> <span class="n">THE</span> <span class="n">SQUALID</span> <span class="n">QUARTER</span> <span class="n">OF</span> <span class="n">THE</span> <span class="n">BROTHELS</span>
<span class="o">./</span><span class="n">tmp</span><span class="o">/</span><span class="n">icefall_asr_librispeech_tdnn</span><span class="o">-</span><span class="n">lstm_ctc</span><span class="o">/</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1221</span><span class="o">-</span><span class="mi">135766</span><span class="o">-</span><span class="mf">0001.</span><span class="n">flac</span><span class="p">:</span>
<span class="n">GOD</span> <span class="n">AS</span> <span class="n">A</span> <span class="n">DIRECT</span> <span class="n">CONSEQUENCE</span> <span class="n">OF</span> <span class="n">THE</span> <span class="n">SIN</span> <span class="n">WHICH</span> <span class="n">MAN</span> <span class="n">THUS</span> <span class="n">PUNISHED</span> <span class="n">HAD</span> <span class="n">GIVEN</span> <span class="n">HER</span> <span class="n">A</span> <span class="n">LOVELY</span> <span class="n">CHILD</span> <span class="n">WHOSE</span> <span class="n">PLACE</span> <span class="n">WAS</span> <span class="n">ON</span> <span class="n">THAT</span> <span class="n">SAME</span> <span class="n">DISHONORED</span> <span class="n">BOSOM</span> <span class="n">TO</span> <span class="n">CONNECT</span> <span class="n">HER</span> <span class="n">PARENT</span> <span class="n">FOREVER</span> <span class="n">WITH</span> <span class="n">THE</span> <span class="n">RACE</span> <span class="n">AND</span> <span class="n">DESCENT</span> <span class="n">OF</span> <span class="n">MORTALS</span> <span class="n">AND</span> <span class="n">TO</span> <span class="n">BE</span> <span class="n">FINALLY</span> <span class="n">A</span> <span class="n">BLESSED</span> <span class="n">SOUL</span> <span class="n">IN</span> <span class="n">HEAVEN</span>
<span class="o">./</span><span class="n">tmp</span><span class="o">/</span><span class="n">icefall_asr_librispeech_tdnn</span><span class="o">-</span><span class="n">lstm_ctc</span><span class="o">/</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1221</span><span class="o">-</span><span class="mi">135766</span><span class="o">-</span><span class="mf">0002.</span><span class="n">flac</span><span class="p">:</span>
<span class="n">YET</span> <span class="n">THESE</span> <span class="n">THOUGHTS</span> <span class="n">AFFECTED</span> <span class="n">HESTER</span> <span class="n">PRYNNE</span> <span class="n">LESS</span> <span class="n">WITH</span> <span class="n">HOPE</span> <span class="n">THAN</span> <span class="n">APPREHENSION</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">57</span><span class="p">:</span><span class="mi">28</span><span class="p">,</span><span class="mi">099</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">268</span><span class="p">]</span> <span class="n">Decoding</span> <span class="n">Done</span>
</pre></div>
</div>
<p>To decode with <code class="docutils literal notranslate"><span class="pre">whole-lattice-rescoring</span></code> methond, you can use</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./tdnn_lstm_ctc/pretrained.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--checkpoint<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--words-file<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--HLG<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--method<span class="w"> </span>whole-lattice-rescoring<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--G<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lm/G_4_gram.pt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--ngram-lm-scale<span class="w"> </span><span class="m">0</span>.8<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac
</pre></div>
</div>
<p>The decoding output is:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">39</span><span class="p">:</span><span class="mi">24</span><span class="p">,</span><span class="mi">725</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">168</span><span class="p">]</span> <span class="n">device</span><span class="p">:</span> <span class="n">cuda</span><span class="p">:</span><span class="mi">0</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">39</span><span class="p">:</span><span class="mi">24</span><span class="p">,</span><span class="mi">725</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">170</span><span class="p">]</span> <span class="n">Creating</span> <span class="n">model</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">39</span><span class="p">:</span><span class="mi">29</span><span class="p">,</span><span class="mi">403</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">182</span><span class="p">]</span> <span class="n">Loading</span> <span class="n">HLG</span> <span class="kn">from</span> <span class="nn">.</span><span class="o">/</span><span class="n">tmp</span><span class="o">/</span><span class="n">icefall_asr_librispeech_tdnn</span><span class="o">-</span><span class="n">lstm_ctc</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">lang_phone</span><span class="o">/</span><span class="n">HLG</span><span class="o">.</span><span class="n">pt</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">39</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span><span class="mi">631</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">190</span><span class="p">]</span> <span class="n">Loading</span> <span class="n">G</span> <span class="kn">from</span> <span class="nn">.</span><span class="o">/</span><span class="n">tmp</span><span class="o">/</span><span class="n">icefall_asr_librispeech_tdnn</span><span class="o">-</span><span class="n">lstm_ctc</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">lm</span><span class="o">/</span><span class="n">G_4_gram</span><span class="o">.</span><span class="n">pt</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">39</span><span class="p">:</span><span class="mi">53</span><span class="p">,</span><span class="mi">098</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">199</span><span class="p">]</span> <span class="n">Constructing</span> <span class="n">Fbank</span> <span class="n">computer</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">39</span><span class="p">:</span><span class="mi">53</span><span class="p">,</span><span class="mi">107</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">209</span><span class="p">]</span> <span class="n">Reading</span> <span class="n">sound</span> <span class="n">files</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac&#39;</span><span class="p">,</span> <span class="s1">&#39;./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac&#39;</span><span class="p">,</span> <span class="s1">&#39;./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac&#39;</span><span class="p">]</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">39</span><span class="p">:</span><span class="mi">53</span><span class="p">,</span><span class="mi">121</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">215</span><span class="p">]</span> <span class="n">Decoding</span> <span class="n">started</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">39</span><span class="p">:</span><span class="mi">53</span><span class="p">,</span><span class="mi">443</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">250</span><span class="p">]</span> <span class="n">Use</span> <span class="n">HLG</span> <span class="n">decoding</span> <span class="o">+</span> <span class="n">LM</span> <span class="n">rescoring</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">39</span><span class="p">:</span><span class="mi">54</span><span class="p">,</span><span class="mi">010</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">266</span><span class="p">]</span>
<span class="o">./</span><span class="n">tmp</span><span class="o">/</span><span class="n">icefall_asr_librispeech_tdnn</span><span class="o">-</span><span class="n">lstm_ctc</span><span class="o">/</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1089</span><span class="o">-</span><span class="mi">134686</span><span class="o">-</span><span class="mf">0001.</span><span class="n">flac</span><span class="p">:</span>
<span class="n">AFTER</span> <span class="n">EARLY</span> <span class="n">NIGHTFALL</span> <span class="n">THE</span> <span class="n">YELLOW</span> <span class="n">LAMPS</span> <span class="n">WOULD</span> <span class="n">LIGHT</span> <span class="n">UP</span> <span class="n">HERE</span> <span class="n">AND</span> <span class="n">THERE</span> <span class="n">THE</span> <span class="n">SQUALID</span> <span class="n">QUARTER</span> <span class="n">OF</span> <span class="n">THE</span> <span class="n">BROTHELS</span>
<span class="o">./</span><span class="n">tmp</span><span class="o">/</span><span class="n">icefall_asr_librispeech_tdnn</span><span class="o">-</span><span class="n">lstm_ctc</span><span class="o">/</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1221</span><span class="o">-</span><span class="mi">135766</span><span class="o">-</span><span class="mf">0001.</span><span class="n">flac</span><span class="p">:</span>
<span class="n">GOD</span> <span class="n">AS</span> <span class="n">A</span> <span class="n">DIRECT</span> <span class="n">CONSEQUENCE</span> <span class="n">OF</span> <span class="n">THE</span> <span class="n">SIN</span> <span class="n">WHICH</span> <span class="n">MAN</span> <span class="n">THUS</span> <span class="n">PUNISHED</span> <span class="n">HAD</span> <span class="n">GIVEN</span> <span class="n">HER</span> <span class="n">A</span> <span class="n">LOVELY</span> <span class="n">CHILD</span> <span class="n">WHOSE</span> <span class="n">PLACE</span> <span class="n">WAS</span> <span class="n">ON</span> <span class="n">THAT</span> <span class="n">SAME</span> <span class="n">DISHONORED</span> <span class="n">BOSOM</span> <span class="n">TO</span> <span class="n">CONNECT</span> <span class="n">HER</span> <span class="n">PARENT</span> <span class="n">FOREVER</span> <span class="n">WITH</span> <span class="n">THE</span> <span class="n">RACE</span> <span class="n">AND</span> <span class="n">DESCENT</span> <span class="n">OF</span> <span class="n">MORTALS</span> <span class="n">AND</span> <span class="n">TO</span> <span class="n">BE</span> <span class="n">FINALLY</span> <span class="n">A</span> <span class="n">BLESSED</span> <span class="n">SOUL</span> <span class="n">IN</span> <span class="n">HEAVEN</span>
<span class="o">./</span><span class="n">tmp</span><span class="o">/</span><span class="n">icefall_asr_librispeech_tdnn</span><span class="o">-</span><span class="n">lstm_ctc</span><span class="o">/</span><span class="n">test_wavs</span><span class="o">/</span><span class="mi">1221</span><span class="o">-</span><span class="mi">135766</span><span class="o">-</span><span class="mf">0002.</span><span class="n">flac</span><span class="p">:</span>
<span class="n">YET</span> <span class="n">THESE</span> <span class="n">THOUGHTS</span> <span class="n">AFFECTED</span> <span class="n">HESTER</span> <span class="n">PRYNNE</span> <span class="n">LESS</span> <span class="n">WITH</span> <span class="n">HOPE</span> <span class="n">THAN</span> <span class="n">APPREHENSION</span>
<span class="mi">2021</span><span class="o">-</span><span class="mi">08</span><span class="o">-</span><span class="mi">24</span> <span class="mi">16</span><span class="p">:</span><span class="mi">39</span><span class="p">:</span><span class="mi">54</span><span class="p">,</span><span class="mi">010</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">pretrained</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">268</span><span class="p">]</span> <span class="n">Decoding</span> <span class="n">Done</span>
</pre></div>
</div>
</section>
</section>
<section id="colab-notebook">
<h2>Colab notebook<a class="headerlink" href="#colab-notebook" title="Permalink to this heading"></a></h2>
<p>We provide a colab notebook for decoding with pre-trained model.</p>
<p><a class="reference external" href="https://colab.research.google.com/drive/1-iSfQMp2So-We_Uu49N4AAcMInB72u9z?usp=sharing"><img alt="librispeech tdnn_lstm_ctc colab notebook" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
<p><strong>Congratulations!</strong> You have finished the TDNN-LSTM-CTC recipe on librispeech in <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
</section>
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