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<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../for-dummies/index.html">Icefall for dummies tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../installation/index.html">Installation</a></li>
<li class="toctree-l1 current"><a class="reference internal" href="index.html">Docker</a><ul class="current">
<li class="toctree-l2 current"><a class="current reference internal" href="#">Introduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="#view-available-tags">View available tags</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#cuda-enabled-docker-images">CUDA-enabled docker images</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#download-a-docker-image-cuda">Download a docker image (CUDA)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#run-a-docker-image-with-gpu">Run a docker image with GPU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#run-yesno-within-a-docker-container">Run yesno within a docker container</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="#data-preparation">Data preparation</a></li>
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<section id="introduction">
<h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading"></a></h1>
<p>We have pre-built docker images hosted at the following address:</p>
<blockquote>
<div><p><a class="reference external" href="https://hub.docker.com/repository/docker/k2fsa/icefall/general">https://hub.docker.com/repository/docker/k2fsa/icefall/general</a></p>
</div></blockquote>
<figure class="align-center">
<a class="reference internal image-reference" href="../_images/docker-hub.png"><img alt="../_images/docker-hub.png" src="../_images/docker-hub.png" style="width: 600px;" /></a>
</figure>
<p>You can find the <code class="docutils literal notranslate"><span class="pre">Dockerfile</span></code> at <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/docker">https://github.com/k2-fsa/icefall/tree/master/docker</a>.</p>
<p>We describe the following items in this section:</p>
<blockquote>
<div><ul class="simple">
<li><p>How to view available tags</p></li>
<li><p>How to download pre-built docker images</p></li>
<li><p>How to run the <a class="reference external" href="https://www.openslr.org/1/">yesno</a> recipe within a docker container on <code class="docutils literal notranslate"><span class="pre">CPU</span></code></p></li>
</ul>
</div></blockquote>
</section>
<section id="view-available-tags">
<h1>View available tags<a class="headerlink" href="#view-available-tags" title="Permalink to this heading"></a></h1>
<section id="cuda-enabled-docker-images">
<h2>CUDA-enabled docker images<a class="headerlink" href="#cuda-enabled-docker-images" title="Permalink to this heading"></a></h2>
<p>You can use the following command to view available tags for CUDA-enabled
docker images:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>curl<span class="w"> </span>-s<span class="w"> </span><span class="s1">&#39;https://registry.hub.docker.com/v2/repositories/k2fsa/icefall/tags/&#39;</span><span class="p">|</span>jq<span class="w"> </span><span class="s1">&#39;.&quot;results&quot;[][&quot;name&quot;]&#39;</span>
</pre></div>
</div>
<p>which will give you something like below:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot;torch2.4.1-cuda12.4&quot;</span>
<span class="s2">&quot;torch2.4.1-cuda12.1&quot;</span>
<span class="s2">&quot;torch2.4.1-cuda11.8&quot;</span>
<span class="s2">&quot;torch2.4.0-cuda12.4&quot;</span>
<span class="s2">&quot;torch2.4.0-cuda12.1&quot;</span>
<span class="s2">&quot;torch2.4.0-cuda11.8&quot;</span>
<span class="s2">&quot;torch2.3.1-cuda12.1&quot;</span>
<span class="s2">&quot;torch2.3.1-cuda11.8&quot;</span>
<span class="s2">&quot;torch2.2.2-cuda12.1&quot;</span>
<span class="s2">&quot;torch2.2.2-cuda11.8&quot;</span>
<span class="s2">&quot;torch2.2.1-cuda12.1&quot;</span>
<span class="s2">&quot;torch2.2.1-cuda11.8&quot;</span>
<span class="s2">&quot;torch2.2.0-cuda12.1&quot;</span>
<span class="s2">&quot;torch2.2.0-cuda11.8&quot;</span>
<span class="s2">&quot;torch2.1.0-cuda12.1&quot;</span>
<span class="s2">&quot;torch2.1.0-cuda11.8&quot;</span>
<span class="s2">&quot;torch2.0.0-cuda11.7&quot;</span>
<span class="s2">&quot;torch1.12.1-cuda11.3&quot;</span>
<span class="s2">&quot;torch1.9.0-cuda10.2&quot;</span>
<span class="s2">&quot;torch1.13.0-cuda11.6&quot;</span>
</pre></div>
</div>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>Available tags will be updated when there are new releases of <a class="reference external" href="https://github.com/pytorch/pytorch">torch</a>.</p>
</div>
<p>Please select an appropriate combination of <a class="reference external" href="https://github.com/pytorch/pytorch">torch</a> and CUDA.</p>
</section>
<section id="cpu-only-docker-images">
<h2>CPU-only docker images<a class="headerlink" href="#cpu-only-docker-images" title="Permalink to this heading"></a></h2>
<p>To view CPU-only docker images, please visit <a class="reference external" href="https://github.com/k2-fsa/icefall/pkgs/container/icefall">https://github.com/k2-fsa/icefall/pkgs/container/icefall</a>
for available tags.</p>
<p>You can select different combinations of <code class="docutils literal notranslate"><span class="pre">Python</span></code> and <code class="docutils literal notranslate"><span class="pre">torch</span></code>. For instance,
to select <code class="docutils literal notranslate"><span class="pre">Python</span> <span class="pre">3.8</span></code> and <code class="docutils literal notranslate"><span class="pre">torch</span> <span class="pre">2.1.2</span></code>, you can use the following tag</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>cpu-py3.8-torch2.1.2-v1.1
</pre></div>
</div>
<p>where <code class="docutils literal notranslate"><span class="pre">v1.1</span></code> is the current version of the docker image. You may see
<code class="docutils literal notranslate"><span class="pre">ghcr.io/k2-fsa/icefall:cpu-py3.8-torch2.1.2-v1.2</span></code> or some other versions.
We recommend that you always use the latest version.</p>
</section>
</section>
<section id="download-a-docker-image-cuda">
<h1>Download a docker image (CUDA)<a class="headerlink" href="#download-a-docker-image-cuda" title="Permalink to this heading"></a></h1>
<p>Suppose that you select the tag <code class="docutils literal notranslate"><span class="pre">torch1.13.0-cuda11.6</span></code>, you can use
the following command to download it:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>sudo<span class="w"> </span>docker<span class="w"> </span>image<span class="w"> </span>pull<span class="w"> </span>k2fsa/icefall:torch1.13.0-cuda11.6
</pre></div>
</div>
</section>
<section id="download-a-docker-image-cpu">
<h1>Download a docker image (CPU)<a class="headerlink" href="#download-a-docker-image-cpu" title="Permalink to this heading"></a></h1>
<p>Suppose that you select the tag <code class="docutils literal notranslate"><span class="pre">cpu-py3.8-torch2.1.2-v1.1</span></code>, you can use
the following command to download it:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>sudo<span class="w"> </span>docker<span class="w"> </span>pull<span class="w"> </span>ghcr.io/k2-fsa/icefall:cpu-py3.8-torch2.1.2-v1.1
</pre></div>
</div>
</section>
<section id="run-a-docker-image-with-gpu">
<h1>Run a docker image with GPU<a class="headerlink" href="#run-a-docker-image-with-gpu" title="Permalink to this heading"></a></h1>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>sudo<span class="w"> </span>docker<span class="w"> </span>run<span class="w"> </span>--gpus<span class="w"> </span>all<span class="w"> </span>--rm<span class="w"> </span>-it<span class="w"> </span>k2fsa/icefall:torch1.13.0-cuda11.6<span class="w"> </span>/bin/bash
</pre></div>
</div>
</section>
<section id="run-a-docker-image-with-cpu">
<h1>Run a docker image with CPU<a class="headerlink" href="#run-a-docker-image-with-cpu" title="Permalink to this heading"></a></h1>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>sudo<span class="w"> </span>docker<span class="w"> </span>run<span class="w"> </span>--rm<span class="w"> </span>-it<span class="w"> </span>ghcr.io/k2-fsa/icefall:cpu-py3.8-torch2.1.2-v1.1<span class="w"> </span>/bin/bash
</pre></div>
</div>
</section>
<section id="run-yesno-within-a-docker-container">
<h1>Run yesno within a docker container<a class="headerlink" href="#run-yesno-within-a-docker-container" title="Permalink to this heading"></a></h1>
<p>After starting the container, the following interface is presented:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># GPU-enabled docker</span>
root@60c947eac59c:/workspace/icefall#
<span class="c1"># CPU-only docker</span>
root@60c947eac59c:#<span class="w"> </span>mkdir<span class="w"> </span>/workspace<span class="p">;</span><span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/k2-fsa/icefall
root@60c947eac59c:#<span class="w"> </span><span class="nb">export</span><span class="w"> </span><span class="nv">PYTHONPATH</span><span class="o">=</span>/workspace/icefall:<span class="nv">$PYTHONPATH</span>
</pre></div>
</div>
<p>It shows the current user is <code class="docutils literal notranslate"><span class="pre">root</span></code> and the current working directory
is <code class="docutils literal notranslate"><span class="pre">/workspace/icefall</span></code>.</p>
<section id="update-the-code">
<h2>Update the code<a class="headerlink" href="#update-the-code" title="Permalink to this heading"></a></h2>
<p>Please first run:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>root@60c947eac59c:/workspace/icefall#<span class="w"> </span>git<span class="w"> </span>pull
</pre></div>
</div>
<p>so that your local copy contains the latest code.</p>
</section>
<section id="data-preparation">
<h2>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this heading"></a></h2>
<p>Now we can use</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>root@60c947eac59c:/workspace/icefall#<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>egs/yesno/ASR/
</pre></div>
</div>
<p>to switch to the <code class="docutils literal notranslate"><span class="pre">yesno</span></code> recipe and run</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>root@60c947eac59c:/workspace/icefall/egs/yesno/ASR#<span class="w"> </span>./prepare.sh
</pre></div>
</div>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<blockquote>
<div><p>If you are running without GPU with a GPU-enabled docker, it may report the following error:</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>File<span class="w"> </span><span class="s2">&quot;/opt/conda/lib/python3.9/site-packages/k2/__init__.py&quot;</span>,<span class="w"> </span>line<span class="w"> </span><span class="m">23</span>,<span class="w"> </span><span class="k">in</span><span class="w"> </span>&lt;module&gt;
<span class="w"> </span>from<span class="w"> </span>_k2<span class="w"> </span>import<span class="w"> </span>DeterminizeWeightPushingType
ImportError:<span class="w"> </span>libcuda.so.1:<span class="w"> </span>cannot<span class="w"> </span>open<span class="w"> </span>shared<span class="w"> </span>object<span class="w"> </span>file:<span class="w"> </span>No<span class="w"> </span>such<span class="w"> </span>file<span class="w"> </span>or<span class="w"> </span>directory
</pre></div>
</div>
</div></blockquote>
</div></blockquote>
<p>We can use the following command to fix it:</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>root@60c947eac59c:/workspace/icefall/egs/yesno/ASR#<span class="w"> </span>ln<span class="w"> </span>-s<span class="w"> </span>/opt/conda/lib/stubs/libcuda.so<span class="w"> </span>/opt/conda/lib/stubs/libcuda.so.1
</pre></div>
</div>
</div></blockquote>
</div>
<p>The logs of running <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> are listed below:</p>
</section>
<section id="training">
<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading"></a></h2>
<p>After preparing the data, we can start training with the following command</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>root@60c947eac59c:/workspace/icefall/egs/yesno/ASR#<span class="w"> </span>./tdnn/train.py
</pre></div>
</div>
<p>All of the training logs are given below:</p>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>It is running on CPU and it takes only 16 seconds for this run.</p>
</div>
</section>
<section id="decoding">
<h2>Decoding<a class="headerlink" href="#decoding" title="Permalink to this heading"></a></h2>
<p>After training, we can decode the trained model with</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>root@60c947eac59c:/workspace/icefall/egs/yesno/ASR#<span class="w"> </span>./tdnn/decode.py
</pre></div>
</div>
<p>The decoding logs are given below:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,400<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>decode.py:263<span class="o">]</span><span class="w"> </span>Decoding<span class="w"> </span>started
<span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,400<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>decode.py:264<span class="o">]</span><span class="w"> </span><span class="o">{</span><span class="s1">&#39;exp_dir&#39;</span>:<span class="w"> </span>PosixPath<span class="o">(</span><span class="s1">&#39;tdnn/exp&#39;</span><span class="o">)</span>,<span class="w"> </span><span class="s1">&#39;lang_dir&#39;</span>:<span class="w"> </span>PosixPath<span class="o">(</span><span class="s1">&#39;data/lang_phone&#39;</span><span class="o">)</span>,<span class="w"> </span><span class="s1">&#39;lm_dir&#39;</span>:<span class="w"> </span>PosixPath<span class="o">(</span><span class="s1">&#39;data/lm&#39;</span><span class="o">)</span>,<span class="w"> </span><span class="s1">&#39;feature_dim&#39;</span>:<span class="w"> </span><span class="m">23</span>,<span class="w"> </span><span class="s1">&#39;search_beam&#39;</span>:<span class="w"> </span><span class="m">20</span>,<span class="w"> </span><span class="s1">&#39;output_beam&#39;</span>:<span class="w"> </span><span class="m">8</span>,<span class="w"> </span><span class="s1">&#39;min_active_states&#39;</span>:<span class="w"> </span><span class="m">30</span>,<span class="w"> </span><span class="s1">&#39;max_active_states&#39;</span>:<span class="w"> </span><span class="m">10000</span>,<span class="w"> </span><span class="s1">&#39;use_double_scores&#39;</span>:<span class="w"> </span>True,<span class="w"> </span><span class="s1">&#39;epoch&#39;</span>:<span class="w"> </span><span class="m">14</span>,<span class="w"> </span><span class="s1">&#39;avg&#39;</span>:<span class="w"> </span><span class="m">2</span>,<span class="w"> </span><span class="s1">&#39;export&#39;</span>:<span class="w"> </span>False,<span class="w"> </span><span class="s1">&#39;feature_dir&#39;</span>:<span class="w"> </span>PosixPath<span class="o">(</span><span class="s1">&#39;data/fbank&#39;</span><span class="o">)</span>,<span class="w"> </span><span class="s1">&#39;max_duration&#39;</span>:<span class="w"> </span><span class="m">30</span>.0,<span class="w"> </span><span class="s1">&#39;bucketing_sampler&#39;</span>:<span class="w"> </span>False,<span class="w"> </span><span class="s1">&#39;num_buckets&#39;</span>:<span class="w"> </span><span class="m">10</span>,<span class="w"> </span><span class="s1">&#39;concatenate_cuts&#39;</span>:<span class="w"> </span>False,<span class="w"> </span><span class="s1">&#39;duration_factor&#39;</span>:<span class="w"> </span><span class="m">1</span>.0,<span class="w"> </span><span class="s1">&#39;gap&#39;</span>:<span class="w"> </span><span class="m">1</span>.0,<span class="w"> </span><span class="s1">&#39;on_the_fly_feats&#39;</span>:<span class="w"> </span>False,<span class="w"> </span><span class="s1">&#39;shuffle&#39;</span>:<span class="w"> </span>False,<span class="w"> </span><span class="s1">&#39;return_cuts&#39;</span>:<span class="w"> </span>True,<span class="w"> </span><span class="s1">&#39;num_workers&#39;</span>:<span class="w"> </span><span class="m">2</span>,<span class="w"> </span><span class="s1">&#39;env_info&#39;</span>:<span class="w"> </span><span class="o">{</span><span class="s1">&#39;k2-version&#39;</span>:<span class="w"> </span><span class="s1">&#39;1.24.3&#39;</span>,<span class="w"> </span><span class="s1">&#39;k2-build-type&#39;</span>:<span class="w"> </span><span class="s1">&#39;Release&#39;</span>,<span class="w"> </span><span class="s1">&#39;k2-with-cuda&#39;</span>:<span class="w"> </span>True,<span class="w"> </span><span class="s1">&#39;k2-git-sha1&#39;</span>:<span class="w"> </span><span class="s1">&#39;4c05309499a08454997adf500b56dcc629e35ae5&#39;</span>,<span class="w"> </span><span class="s1">&#39;k2-git-date&#39;</span>:<span class="w"> </span><span class="s1">&#39;Tue Jul 25 16:23:36 2023&#39;</span>,<span class="w"> </span><span class="s1">&#39;lhotse-version&#39;</span>:<span class="w"> </span><span class="s1">&#39;1.16.0.dev+git.7640d663.clean&#39;</span>,<span class="w"> </span><span class="s1">&#39;torch-version&#39;</span>:<span class="w"> </span><span class="s1">&#39;1.13.0&#39;</span>,<span class="w"> </span><span class="s1">&#39;torch-cuda-available&#39;</span>:<span class="w"> </span>False,<span class="w"> </span><span class="s1">&#39;torch-cuda-version&#39;</span>:<span class="w"> </span><span class="s1">&#39;11.6&#39;</span>,<span class="w"> </span><span class="s1">&#39;python-version&#39;</span>:<span class="w"> </span><span class="s1">&#39;3.9&#39;</span>,<span class="w"> </span><span class="s1">&#39;icefall-git-branch&#39;</span>:<span class="w"> </span><span class="s1">&#39;master&#39;</span>,<span class="w"> </span><span class="s1">&#39;icefall-git-sha1&#39;</span>:<span class="w"> </span><span class="s1">&#39;375520d-clean&#39;</span>,<span class="w"> </span><span class="s1">&#39;icefall-git-date&#39;</span>:<span class="w"> </span><span class="s1">&#39;Fri Jul 28 07:43:08 2023&#39;</span>,<span class="w"> </span><span class="s1">&#39;icefall-path&#39;</span>:<span class="w"> </span><span class="s1">&#39;/workspace/icefall&#39;</span>,<span class="w"> </span><span class="s1">&#39;k2-path&#39;</span>:<span class="w"> </span><span class="s1">&#39;/opt/conda/lib/python3.9/site-packages/k2/__init__.py&#39;</span>,<span class="w"> </span><span class="s1">&#39;lhotse-path&#39;</span>:<span class="w"> </span><span class="s1">&#39;/opt/conda/lib/python3.9/site-packages/lhotse/__init__.py&#39;</span>,<span class="w"> </span><span class="s1">&#39;hostname&#39;</span>:<span class="w"> </span><span class="s1">&#39;60c947eac59c&#39;</span>,<span class="w"> </span><span class="s1">&#39;IP address&#39;</span>:<span class="w"> </span><span class="s1">&#39;172.17.0.2&#39;</span><span class="o">}}</span>
<span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,401<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>lexicon.py:168<span class="o">]</span><span class="w"> </span>Loading<span class="w"> </span>pre-compiled<span class="w"> </span>data/lang_phone/Linv.pt
<span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,403<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>decode.py:273<span class="o">]</span><span class="w"> </span>device:<span class="w"> </span>cpu
<span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,406<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>decode.py:291<span class="o">]</span><span class="w"> </span>averaging<span class="w"> </span><span class="o">[</span><span class="s1">&#39;tdnn/exp/epoch-13.pt&#39;</span>,<span class="w"> </span><span class="s1">&#39;tdnn/exp/epoch-14.pt&#39;</span><span class="o">]</span>
<span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,424<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>asr_datamodule.py:218<span class="o">]</span><span class="w"> </span>About<span class="w"> </span>to<span class="w"> </span>get<span class="w"> </span><span class="nb">test</span><span class="w"> </span>cuts
<span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,425<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>asr_datamodule.py:252<span class="o">]</span><span class="w"> </span>About<span class="w"> </span>to<span class="w"> </span>get<span class="w"> </span><span class="nb">test</span><span class="w"> </span>cuts
<span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,504<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>decode.py:204<span class="o">]</span><span class="w"> </span>batch<span class="w"> </span><span class="m">0</span>/?,<span class="w"> </span>cuts<span class="w"> </span>processed<span class="w"> </span><span class="k">until</span><span class="w"> </span>now<span class="w"> </span>is<span class="w"> </span><span class="m">4</span>
<span class="o">[</span>W<span class="w"> </span>NNPACK.cpp:53<span class="o">]</span><span class="w"> </span>Could<span class="w"> </span>not<span class="w"> </span>initialize<span class="w"> </span>NNPACK!<span class="w"> </span>Reason:<span class="w"> </span>Unsupported<span class="w"> </span>hardware.
<span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,687<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>decode.py:241<span class="o">]</span><span class="w"> </span>The<span class="w"> </span>transcripts<span class="w"> </span>are<span class="w"> </span>stored<span class="w"> </span><span class="k">in</span><span class="w"> </span>tdnn/exp/recogs-test_set.txt
<span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,688<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>utils.py:564<span class="o">]</span><span class="w"> </span><span class="o">[</span>test_set<span class="o">]</span><span class="w"> </span>%WER<span class="w"> </span><span class="m">0</span>.42%<span class="w"> </span><span class="o">[</span><span class="m">1</span><span class="w"> </span>/<span class="w"> </span><span class="m">240</span>,<span class="w"> </span><span class="m">0</span><span class="w"> </span>ins,<span class="w"> </span><span class="m">1</span><span class="w"> </span>del,<span class="w"> </span><span class="m">0</span><span class="w"> </span>sub<span class="w"> </span><span class="o">]</span>
<span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,690<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>decode.py:249<span class="o">]</span><span class="w"> </span>Wrote<span class="w"> </span>detailed<span class="w"> </span>error<span class="w"> </span>stats<span class="w"> </span>to<span class="w"> </span>tdnn/exp/errs-test_set.txt
<span class="m">2023</span>-08-01<span class="w"> </span><span class="m">02</span>:06:22,690<span class="w"> </span>INFO<span class="w"> </span><span class="o">[</span>decode.py:316<span class="o">]</span><span class="w"> </span>Done!
</pre></div>
</div>
<p>Congratulations! You have finished successfully running <a class="reference external" href="https://github.com/k2-fsa/icefall">icefall</a> within a docker container.</p>
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