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<li class="toctree-l2"><a class="reference internal" href="export-model-state-dict.html">Export model.state_dict()</a></li>
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<li class="toctree-l2"><a class="reference internal" href="export-with-torch-jit-trace.html">Export model with torch.jit.trace()</a></li>
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<li class="toctree-l2"><a class="reference internal" href="export-with-torch-jit-script.html">Export model with torch.jit.script()</a></li>
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<li class="toctree-l2 current"><a class="current reference internal" href="#">Export to ONNX</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="#sherpa-onnx">sherpa-onnx</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#example">Example</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#download-the-pre-trained-model">Download the pre-trained model</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#export-the-model-to-onnx">Export the model to ONNX</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#decode-sound-files-with-exported-onnx-models">Decode sound files with exported ONNX models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="export-ncnn.html">Export to ncnn</a></li>
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<li class="breadcrumb-item active">Export to ONNX</li>
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<section id="export-to-onnx">
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<h1>Export to ONNX<a class="headerlink" href="#export-to-onnx" title="Permalink to this heading"></a></h1>
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<p>In this section, we describe how to export models to <a class="reference external" href="https://github.com/onnx/onnx">ONNX</a>.</p>
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<div class="admonition hint">
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<p class="admonition-title">Hint</p>
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<p>Before you continue, please run:</p>
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<blockquote>
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<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>onnx
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</pre></div>
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</div>
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</div></blockquote>
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</div>
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<p>In each recipe, there is a file called <code class="docutils literal notranslate"><span class="pre">export-onnx.py</span></code>, which is used
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to export trained models to <a class="reference external" href="https://github.com/onnx/onnx">ONNX</a>.</p>
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<p>There is also a file named <code class="docutils literal notranslate"><span class="pre">onnx_pretrained.py</span></code>, which you can use
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the exported <a class="reference external" href="https://github.com/onnx/onnx">ONNX</a> model in Python with <a class="reference external" href="https://github.com/microsoft/onnxruntime">onnxruntime</a> to decode sound files.</p>
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<section id="sherpa-onnx">
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<h2>sherpa-onnx<a class="headerlink" href="#sherpa-onnx" title="Permalink to this heading"></a></h2>
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<p>We have a separate repository <a class="reference external" href="https://github.com/k2-fsa/sherpa-onnx">sherpa-onnx</a> for deploying your exported models
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on various platforms such as:</p>
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<blockquote>
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<div><ul class="simple">
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<li><p>iOS</p></li>
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<li><p>Android</p></li>
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<li><p>Raspberry Pi</p></li>
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<li><p>Linux/macOS/Windows</p></li>
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</ul>
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</div></blockquote>
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<p>Please see the documentation of <a class="reference external" href="https://github.com/k2-fsa/sherpa-onnx">sherpa-onnx</a> for details:</p>
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<blockquote>
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<div><p><a class="reference external" href="https://k2-fsa.github.io/sherpa/onnx/index.html">https://k2-fsa.github.io/sherpa/onnx/index.html</a></p>
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</div></blockquote>
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</section>
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<section id="example">
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<h2>Example<a class="headerlink" href="#example" title="Permalink to this heading"></a></h2>
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<p>In the following, we demonstrate how to export a streaming Zipformer pre-trained
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model from
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<a class="reference external" href="https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11">https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11</a>
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to <a class="reference external" href="https://github.com/onnx/onnx">ONNX</a>.</p>
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</section>
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<section id="download-the-pre-trained-model">
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<h2>Download the pre-trained model<a class="headerlink" href="#download-the-pre-trained-model" title="Permalink to this heading"></a></h2>
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<div class="admonition hint">
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<p class="admonition-title">Hint</p>
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<p>We assume you have installed <a class="reference external" href="https://git-lfs.com/">git-lfs</a>.</p>
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</div>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
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<span class="nv">repo_url</span><span class="o">=</span>https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
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<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><span class="nv">$repo_url</span>
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<span class="nv">repo</span><span class="o">=</span><span class="k">$(</span>basename<span class="w"> </span><span class="nv">$repo_url</span><span class="k">)</span>
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<span class="nb">pushd</span><span class="w"> </span><span class="nv">$repo</span>
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git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span><span class="s2">"data/lang_bpe_500/bpe.model"</span>
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git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span><span class="s2">"exp/pretrained.pt"</span>
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<span class="nb">cd</span><span class="w"> </span>exp
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ln<span class="w"> </span>-s<span class="w"> </span>pretrained.pt<span class="w"> </span>epoch-99.pt
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<span class="nb">popd</span>
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</pre></div>
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</div>
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</section>
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<section id="export-the-model-to-onnx">
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<h2>Export the model to ONNX<a class="headerlink" href="#export-the-model-to-onnx" title="Permalink to this heading"></a></h2>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./pruned_transducer_stateless7_streaming/export-onnx.py<span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--tokens<span class="w"> </span><span class="nv">$repo</span>/data/lang_bpe_500/tokens.txt<span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--use-averaged-model<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--epoch<span class="w"> </span><span class="m">99</span><span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--avg<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--decode-chunk-len<span class="w"> </span><span class="m">32</span><span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--exp-dir<span class="w"> </span><span class="nv">$repo</span>/exp/
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</pre></div>
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</div>
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<div class="admonition warning">
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<p class="admonition-title">Warning</p>
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<p><code class="docutils literal notranslate"><span class="pre">export-onnx.py</span></code> from different recipes has different options.</p>
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<p>In the above example, <code class="docutils literal notranslate"><span class="pre">--decode-chunk-len</span></code> is specific for the
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streaming Zipformer. Other models won’t have such an option.</p>
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</div>
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<p>It will generate the following 3 files in <code class="docutils literal notranslate"><span class="pre">$repo/exp</span></code></p>
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<blockquote>
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<div><ul class="simple">
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<li><p><code class="docutils literal notranslate"><span class="pre">encoder-epoch-99-avg-1.onnx</span></code></p></li>
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<li><p><code class="docutils literal notranslate"><span class="pre">decoder-epoch-99-avg-1.onnx</span></code></p></li>
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<li><p><code class="docutils literal notranslate"><span class="pre">joiner-epoch-99-avg-1.onnx</span></code></p></li>
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</ul>
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</div></blockquote>
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</section>
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<section id="decode-sound-files-with-exported-onnx-models">
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<h2>Decode sound files with exported ONNX models<a class="headerlink" href="#decode-sound-files-with-exported-onnx-models" title="Permalink to this heading"></a></h2>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./pruned_transducer_stateless7_streaming/onnx_pretrained.py<span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--encoder-model-filename<span class="w"> </span><span class="nv">$repo</span>/exp/encoder-epoch-99-avg-1.onnx<span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--decoder-model-filename<span class="w"> </span><span class="nv">$repo</span>/exp/decoder-epoch-99-avg-1.onnx<span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--joiner-model-filename<span class="w"> </span><span class="nv">$repo</span>/exp/joiner-epoch-99-avg-1.onnx<span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--tokens<span class="w"> </span><span class="nv">$repo</span>/data/lang_bpe_500/tokens.txt<span class="w"> </span><span class="se">\</span>
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<span class="w"> </span><span class="nv">$repo</span>/test_wavs/1089-134686-0001.wav
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</pre></div>
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</div>
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