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<li class="toctree-l1 current"><a class="reference internal" href="index.html">Model export</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="export-model-state-dict.html">Export model.state_dict()</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-with-torch-jit-trace.html">Export model with torch.jit.trace()</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-with-torch-jit-script.html">Export model with torch.jit.script()</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-onnx.html">Export to ONNX</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">Export to ncnn</a><ul>
<li class="toctree-l3"><a class="reference internal" href="export-ncnn-zipformer.html">Export streaming Zipformer transducer models to ncnn</a></li>
<li class="toctree-l3"><a class="reference internal" href="export-ncnn-conv-emformer.html">Export ConvEmformer transducer models to ncnn</a></li>
<li class="toctree-l3"><a class="reference internal" href="export-ncnn-lstm.html">Export LSTM transducer models to ncnn</a></li>
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<section id="export-to-ncnn">
<span id="icefall-export-to-ncnn"></span><h1>Export to ncnn<a class="headerlink" href="#export-to-ncnn" title="Permalink to this heading"></a></h1>
<p>We support exporting the following models
to <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a>:</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless7_streaming">Zipformer transducer models</a></p></li>
<li><p><a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/lstm_transducer_stateless2">LSTM transducer models</a></p></li>
<li><p><a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/conv_emformer_transducer_stateless2">ConvEmformer transducer models</a></p></li>
</ul>
</div></blockquote>
<p>We also provide <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>
for performing speech recognition using <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> with exported models.
It has been tested on the following platforms:</p>
<blockquote>
<div><ul class="simple">
<li><p>Linux</p></li>
<li><p>macOS</p></li>
<li><p>Windows</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Android</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">iOS</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Raspberry</span> <span class="pre">Pi</span></code></p></li>
<li><p><a class="reference external" href="https://wiki.sipeed.com/hardware/zh/">爱芯派</a> (<a class="reference external" href="https://wiki.sipeed.com/hardware/en/maixIII/ax-pi/axpi.html">MAIX-III AXera-Pi</a>).</p></li>
<li><p><a class="reference external" href="https://www.rock-chips.com/a/en/products/RV11_Series/2020/0427/1076.html">RV1126</a></p></li>
</ul>
</div></blockquote>
<p><a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a> is self-contained and can be statically linked to produce
a binary containing everything needed. Please refer
to its documentation for details:</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference external" href="https://k2-fsa.github.io/sherpa/ncnn/index.html">https://k2-fsa.github.io/sherpa/ncnn/index.html</a></p></li>
</ul>
</div></blockquote>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="export-ncnn-zipformer.html">Export streaming Zipformer transducer models to ncnn</a><ul>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-zipformer.html#download-the-pre-trained-model">1. Download the pre-trained model</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-zipformer.html#install-ncnn-and-pnnx">2. Install ncnn and pnnx</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-zipformer.html#export-the-model-via-torch-jit-trace">3. Export the model via torch.jit.trace()</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-zipformer.html#export-torchscript-model-via-pnnx">4. Export torchscript model via pnnx</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-zipformer.html#test-the-exported-models-in-icefall">5. Test the exported models in icefall</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-zipformer.html#modify-the-exported-encoder-for-sherpa-ncnn">6. Modify the exported encoder for sherpa-ncnn</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="export-ncnn-conv-emformer.html">Export ConvEmformer transducer models to ncnn</a><ul>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-conv-emformer.html#download-the-pre-trained-model">1. Download the pre-trained model</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-conv-emformer.html#install-ncnn-and-pnnx">2. Install ncnn and pnnx</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-conv-emformer.html#export-the-model-via-torch-jit-trace">3. Export the model via torch.jit.trace()</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-conv-emformer.html#export-torchscript-model-via-pnnx">4. Export torchscript model via pnnx</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-conv-emformer.html#test-the-exported-models-in-icefall">5. Test the exported models in icefall</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-conv-emformer.html#modify-the-exported-encoder-for-sherpa-ncnn">6. Modify the exported encoder for sherpa-ncnn</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-conv-emformer.html#optional-int8-quantization-with-sherpa-ncnn">7. (Optional) int8 quantization with sherpa-ncnn</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="export-ncnn-lstm.html">Export LSTM transducer models to ncnn</a><ul>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-lstm.html#download-the-pre-trained-model">1. Download the pre-trained model</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-lstm.html#install-ncnn-and-pnnx">2. Install ncnn and pnnx</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-lstm.html#export-the-model-via-torch-jit-trace">3. Export the model via torch.jit.trace()</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-lstm.html#export-torchscript-model-via-pnnx">4. Export torchscript model via pnnx</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-lstm.html#test-the-exported-models-in-icefall">5. Test the exported models in icefall</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-lstm.html#modify-the-exported-encoder-for-sherpa-ncnn">6. Modify the exported encoder for sherpa-ncnn</a></li>
<li class="toctree-l2"><a class="reference internal" href="export-ncnn-lstm.html#optional-int8-quantization-with-sherpa-ncnn">7. (Optional) int8 quantization with sherpa-ncnn</a></li>
</ul>
</li>
</ul>
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