mirrors/icefall
1
0
Fork 0
mirror of https://github.com/k2-fsa/icefall.git synced 2025-08-09 18:12:19 +00:00
Code Issues Packages Projects Releases Wiki Activity
icefall/model-export/export-ncnn-lstm.html

827 lines
113 KiB
HTML
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

<!DOCTYPE html>
<html class="writer-html5" lang="en" >
<head>
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Export LSTM transducer models to ncnn &mdash; icefall 0.1 documentation</title>
<link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
<!--[if lt IE 9]>
<script src="../_static/js/html5shiv.min.js"></script>
<![endif]-->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.6.0/jquery.min.js"></script>
<script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
<script src="../_static/doctools.js"></script>
<script src="../_static/sphinx_highlight.js"></script>
<script src="../_static/js/theme.js"></script>
<link rel="index" title="Index" href="../genindex.html" />
<link rel="search" title="Search" href="../search.html" />
<link rel="next" title="Recipes" href="../recipes/index.html" />
<link rel="prev" title="Export ConvEmformer transducer models to ncnn" href="export-ncnn-conv-emformer.html" />
</head>
<body class="wy-body-for-nav">
<div class="wy-grid-for-nav">
<nav data-toggle="wy-nav-shift" class="wy-nav-side">
<div class="wy-side-scroll">
<div class="wy-side-nav-search" >
<a href="../index.html" class="icon icon-home">
icefall
</a>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
<input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../installation/index.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../faqs.html">Frequently Asked Questions (FAQs)</a></li>
<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="reference internal" href="export-ncnn.html">Export to ncnn</a><ul class="current">
<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 current"><a class="current reference internal" href="#">Export LSTM transducer models to ncnn</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#download-the-pre-trained-model">1. Download the pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="#install-ncnn-and-pnnx">2. Install ncnn and pnnx</a></li>
<li class="toctree-l4"><a class="reference internal" href="#export-the-model-via-torch-jit-trace">3. Export the model via torch.jit.trace()</a></li>
<li class="toctree-l4"><a class="reference internal" href="#export-torchscript-model-via-pnnx">4. Export torchscript model via pnnx</a></li>
<li class="toctree-l4"><a class="reference internal" href="#test-the-exported-models-in-icefall">5. Test the exported models in icefall</a></li>
<li class="toctree-l4"><a class="reference internal" href="#modify-the-exported-encoder-for-sherpa-ncnn">6. Modify the exported encoder for sherpa-ncnn</a></li>
<li class="toctree-l4"><a class="reference internal" href="#optional-int8-quantization-with-sherpa-ncnn">7. (Optional) int8 quantization with sherpa-ncnn</a></li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../contributing/index.html">Contributing</a></li>
<li class="toctree-l1"><a class="reference internal" href="../huggingface/index.html">Huggingface</a></li>
</ul>
</div>
</div>
</nav>
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
<i data-toggle="wy-nav-top" class="fa fa-bars"></i>
<a href="../index.html">icefall</a>
</nav>
<div class="wy-nav-content">
<div class="rst-content">
<div role="navigation" aria-label="Page navigation">
<ul class="wy-breadcrumbs">
<li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
<li class="breadcrumb-item"><a href="index.html">Model export</a></li>
<li class="breadcrumb-item"><a href="export-ncnn.html">Export to ncnn</a></li>
<li class="breadcrumb-item active">Export LSTM transducer models to ncnn</li>
<li class="wy-breadcrumbs-aside">
<a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/model-export/export-ncnn-lstm.rst" class="fa fa-github"> Edit on GitHub</a>
</li>
</ul>
<hr/>
</div>
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div itemprop="articleBody">
<section id="export-lstm-transducer-models-to-ncnn">
<span id="id1"></span><h1>Export LSTM transducer models to ncnn<a class="headerlink" href="#export-lstm-transducer-models-to-ncnn" title="Permalink to this heading"></a></h1>
<p>We use the pre-trained model from the following repository as an example:</p>
<p><a class="reference external" href="https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03">https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03</a></p>
<p>We will show you step by step how to export it to <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> and run it with <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>.</p>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>We use <code class="docutils literal notranslate"><span class="pre">Ubuntu</span> <span class="pre">18.04</span></code>, <code class="docutils literal notranslate"><span class="pre">torch</span> <span class="pre">1.13</span></code>, and <code class="docutils literal notranslate"><span class="pre">Python</span> <span class="pre">3.8</span></code> for testing.</p>
</div>
<div class="admonition caution">
<p class="admonition-title">Caution</p>
<p>Please use a more recent version of PyTorch. For instance, <code class="docutils literal notranslate"><span class="pre">torch</span> <span class="pre">1.8</span></code>
may <code class="docutils literal notranslate"><span class="pre">not</span></code> work.</p>
</div>
<section id="download-the-pre-trained-model">
<h2>1. Download the pre-trained model<a class="headerlink" href="#download-the-pre-trained-model" title="Permalink to this heading"></a></h2>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>You have to install <a class="reference external" href="https://git-lfs.com/">git-lfs</a> before you continue.</p>
</div>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
<span class="nv">GIT_LFS_SKIP_SMUDGE</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
<span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span><span class="s2">&quot;exp/pretrained-iter-468000-avg-16.pt&quot;</span>
git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span><span class="s2">&quot;data/lang_bpe_500/bpe.model&quot;</span>
<span class="nb">cd</span><span class="w"> </span>..
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We downloaded <code class="docutils literal notranslate"><span class="pre">exp/pretrained-xxx.pt</span></code>, not <code class="docutils literal notranslate"><span class="pre">exp/cpu-jit_xxx.pt</span></code>.</p>
</div>
<p>In the above code, we downloaded the pre-trained model into the directory
<code class="docutils literal notranslate"><span class="pre">egs/librispeech/ASR/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03</span></code>.</p>
</section>
<section id="install-ncnn-and-pnnx">
<h2>2. Install ncnn and pnnx<a class="headerlink" href="#install-ncnn-and-pnnx" title="Permalink to this heading"></a></h2>
<p>Please refer to <a class="reference internal" href="export-ncnn-conv-emformer.html#export-for-ncnn-install-ncnn-and-pnnx"><span class="std std-ref">2. Install ncnn and pnnx</span></a> .</p>
</section>
<section id="export-the-model-via-torch-jit-trace">
<h2>3. Export the model via torch.jit.trace()<a class="headerlink" href="#export-the-model-via-torch-jit-trace" title="Permalink to this heading"></a></h2>
<p>First, let us rename our pre-trained model:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cd</span> <span class="n">egs</span><span class="o">/</span><span class="n">librispeech</span><span class="o">/</span><span class="n">ASR</span>
<span class="n">cd</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</span><span class="o">/</span><span class="n">exp</span>
<span class="n">ln</span> <span class="o">-</span><span class="n">s</span> <span class="n">pretrained</span><span class="o">-</span><span class="nb">iter</span><span class="o">-</span><span class="mi">468000</span><span class="o">-</span><span class="n">avg</span><span class="o">-</span><span class="mf">16.</span><span class="n">pt</span> <span class="n">epoch</span><span class="o">-</span><span class="mf">99.</span><span class="n">pt</span>
<span class="n">cd</span> <span class="o">../..</span>
</pre></div>
</div>
<p>Next, we use the following code to export our model:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">dir</span><span class="o">=</span>./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
./lstm_transducer_stateless2/export-for-ncnn.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--exp-dir<span class="w"> </span><span class="nv">$dir</span>/exp<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--bpe-model<span class="w"> </span><span class="nv">$dir</span>/data/lang_bpe_500/bpe.model<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--epoch<span class="w"> </span><span class="m">99</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--avg<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-averaged-model<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--num-encoder-layers<span class="w"> </span><span class="m">12</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--encoder-dim<span class="w"> </span><span class="m">512</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--rnn-hidden-size<span class="w"> </span><span class="m">1024</span>
</pre></div>
</div>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>We have renamed our model to <code class="docutils literal notranslate"><span class="pre">epoch-99.pt</span></code> so that we can use <code class="docutils literal notranslate"><span class="pre">--epoch</span> <span class="pre">99</span></code>.
There is only one pre-trained model, so we use <code class="docutils literal notranslate"><span class="pre">--avg</span> <span class="pre">1</span> <span class="pre">--use-averaged-model</span> <span class="pre">0</span></code>.</p>
<p>If you have trained a model by yourself and if you have all checkpoints
available, please first use <code class="docutils literal notranslate"><span class="pre">decode.py</span></code> to tune <code class="docutils literal notranslate"><span class="pre">--epoch</span> <span class="pre">--avg</span></code>
and select the best combination with with <code class="docutils literal notranslate"><span class="pre">--use-averaged-model</span> <span class="pre">1</span></code>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>You will see the following log output:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">42</span><span class="p">,</span><span class="mi">862</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">222</span><span class="p">]</span> <span class="n">device</span><span class="p">:</span> <span class="n">cpu</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">42</span><span class="p">,</span><span class="mi">865</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">231</span><span class="p">]</span> <span class="p">{</span><span class="s1">&#39;best_train_loss&#39;</span><span class="p">:</span> <span class="n">inf</span><span class="p">,</span> <span class="s1">&#39;best_valid_loss&#39;</span><span class="p">:</span> <span class="n">inf</span><span class="p">,</span> <span class="s1">&#39;best_train_epoch&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;best_valid_epoch&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;batch_idx_train&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;log_interval&#39;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span> <span class="s1">&#39;reset_interval&#39;</span><span class="p">:</span> <span class="mi">200</span><span class="p">,</span> <span class="s1">&#39;valid_interval&#39;</span><span class="p">:</span> <span class="mi">3000</span><span class="p">,</span> <span class="s1">&#39;feature_dim&#39;</span><span class="p">:</span> <span class="mi">80</span><span class="p">,</span> <span class="s1">&#39;subsampling_factor&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s1">&#39;dim_feedforward&#39;</span><span class="p">:</span> <span class="mi">2048</span><span class="p">,</span> <span class="s1">&#39;decoder_dim&#39;</span><span class="p">:</span> <span class="mi">512</span><span class="p">,</span> <span class="s1">&#39;joiner_dim&#39;</span><span class="p">:</span> <span class="mi">512</span><span class="p">,</span> <span class="s1">&#39;is_pnnx&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">&#39;model_warm_step&#39;</span><span class="p">:</span> <span class="mi">3000</span><span class="p">,</span> <span class="s1">&#39;env_info&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;k2-version&#39;</span><span class="p">:</span> <span class="s1">&#39;1.23.4&#39;</span><span class="p">,</span> <span class="s1">&#39;k2-build-type&#39;</span><span class="p">:</span> <span class="s1">&#39;Release&#39;</span><span class="p">,</span> <span class="s1">&#39;k2-with-cuda&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;k2-git-sha1&#39;</span><span class="p">:</span> <span class="s1">&#39;62e404dd3f3a811d73e424199b3408e309c06e1a&#39;</span><span class="p">,</span> <span class="s1">&#39;k2-git-date&#39;</span><span class="p">:</span> <span class="s1">&#39;Mon Jan 30 10:26:16 2023&#39;</span><span class="p">,</span> <span class="s1">&#39;lhotse-version&#39;</span><span class="p">:</span> <span class="s1">&#39;1.12.0.dev+missing.version.file&#39;</span><span class="p">,</span> <span class="s1">&#39;torch-version&#39;</span><span class="p">:</span> <span class="s1">&#39;1.10.0+cu102&#39;</span><span class="p">,</span> <span class="s1">&#39;torch-cuda-available&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">&#39;torch-cuda-version&#39;</span><span class="p">:</span> <span class="s1">&#39;10.2&#39;</span><span class="p">,</span> <span class="s1">&#39;python-version&#39;</span><span class="p">:</span> <span class="s1">&#39;3.8&#39;</span><span class="p">,</span> <span class="s1">&#39;icefall-git-branch&#39;</span><span class="p">:</span> <span class="s1">&#39;master&#39;</span><span class="p">,</span> <span class="s1">&#39;icefall-git-sha1&#39;</span><span class="p">:</span> <span class="s1">&#39;6d7a559-dirty&#39;</span><span class="p">,</span> <span class="s1">&#39;icefall-git-date&#39;</span><span class="p">:</span> <span class="s1">&#39;Thu Feb 16 19:47:54 2023&#39;</span><span class="p">,</span> <span class="s1">&#39;icefall-path&#39;</span><span class="p">:</span> <span class="s1">&#39;/star-fj/fangjun/open-source/icefall-2&#39;</span><span class="p">,</span> <span class="s1">&#39;k2-path&#39;</span><span class="p">:</span> <span class="s1">&#39;/star-fj/fangjun/open-source/k2/k2/python/k2/__init__.py&#39;</span><span class="p">,</span> <span class="s1">&#39;lhotse-path&#39;</span><span class="p">:</span> <span class="s1">&#39;/star-fj/fangjun/open-source/lhotse/lhotse/__init__.py&#39;</span><span class="p">,</span> <span class="s1">&#39;hostname&#39;</span><span class="p">:</span> <span class="s1">&#39;de-74279-k2-train-3-1220120619-7695ff496b-s9n4w&#39;</span><span class="p">,</span> <span class="s1">&#39;IP address&#39;</span><span class="p">:</span> <span class="s1">&#39;10.177.6.147&#39;</span><span class="p">},</span> <span class="s1">&#39;epoch&#39;</span><span class="p">:</span> <span class="mi">99</span><span class="p">,</span> <span class="s1">&#39;iter&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;avg&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;exp_dir&#39;</span><span class="p">:</span> <span class="n">PosixPath</span><span class="p">(</span><span class="s1">&#39;icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp&#39;</span><span class="p">),</span> <span class="s1">&#39;bpe_model&#39;</span><span class="p">:</span> <span class="s1">&#39;./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/data/lang_bpe_500/bpe.model&#39;</span><span class="p">,</span> <span class="s1">&#39;context_size&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">&#39;use_averaged_model&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">&#39;num_encoder_layers&#39;</span><span class="p">:</span> <span class="mi">12</span><span class="p">,</span> <span class="s1">&#39;encoder_dim&#39;</span><span class="p">:</span> <span class="mi">512</span><span class="p">,</span> <span class="s1">&#39;rnn_hidden_size&#39;</span><span class="p">:</span> <span class="mi">1024</span><span class="p">,</span> <span class="s1">&#39;aux_layer_period&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;blank_id&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;vocab_size&#39;</span><span class="p">:</span> <span class="mi">500</span><span class="p">}</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">42</span><span class="p">,</span><span class="mi">865</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">235</span><span class="p">]</span> <span class="n">About</span> <span class="n">to</span> <span class="n">create</span> <span class="n">model</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">43</span><span class="p">,</span><span class="mi">239</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">train</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">472</span><span class="p">]</span> <span class="n">Disable</span> <span class="n">giga</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">43</span><span class="p">,</span><span class="mi">249</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">112</span><span class="p">]</span> <span class="n">Loading</span> <span class="n">checkpoint</span> <span class="kn">from</span> <span class="nn">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span><span class="n">epoch</span><span class="o">-</span><span class="mf">99.</span><span class="n">pt</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">44</span><span class="p">,</span><span class="mi">595</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">324</span><span class="p">]</span> <span class="n">encoder</span> <span class="n">parameters</span><span class="p">:</span> <span class="mi">83137520</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">44</span><span class="p">,</span><span class="mi">596</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">325</span><span class="p">]</span> <span class="n">decoder</span> <span class="n">parameters</span><span class="p">:</span> <span class="mi">257024</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">44</span><span class="p">,</span><span class="mi">596</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">326</span><span class="p">]</span> <span class="n">joiner</span> <span class="n">parameters</span><span class="p">:</span> <span class="mi">781812</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">44</span><span class="p">,</span><span class="mi">596</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">327</span><span class="p">]</span> <span class="n">total</span> <span class="n">parameters</span><span class="p">:</span> <span class="mi">84176356</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">44</span><span class="p">,</span><span class="mi">596</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">329</span><span class="p">]</span> <span class="n">Using</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">()</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">44</span><span class="p">,</span><span class="mi">596</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">331</span><span class="p">]</span> <span class="n">Exporting</span> <span class="n">encoder</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">48</span><span class="p">,</span><span class="mi">182</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">158</span><span class="p">]</span> <span class="n">Saved</span> <span class="n">to</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span><span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">48</span><span class="p">,</span><span class="mi">183</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">335</span><span class="p">]</span> <span class="n">Exporting</span> <span class="n">decoder</span>
<span class="o">/</span><span class="n">star</span><span class="o">-</span><span class="n">fj</span><span class="o">/</span><span class="n">fangjun</span><span class="o">/</span><span class="nb">open</span><span class="o">-</span><span class="n">source</span><span class="o">/</span><span class="n">icefall</span><span class="o">-</span><span class="mi">2</span><span class="o">/</span><span class="n">egs</span><span class="o">/</span><span class="n">librispeech</span><span class="o">/</span><span class="n">ASR</span><span class="o">/</span><span class="n">lstm_transducer_stateless2</span><span class="o">/</span><span class="n">decoder</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">101</span><span class="p">:</span> <span class="n">TracerWarning</span><span class="p">:</span> <span class="n">Converting</span> <span class="n">a</span> <span class="n">tensor</span> <span class="n">to</span> <span class="n">a</span> <span class="n">Python</span> <span class="n">boolean</span> <span class="n">might</span> <span class="n">cause</span> <span class="n">the</span> <span class="n">trace</span> <span class="n">to</span> <span class="n">be</span> <span class="n">incorrect</span><span class="o">.</span> <span class="n">We</span> <span class="n">can</span><span class="s1">&#39;t record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!</span>
<span class="n">need_pad</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">need_pad</span><span class="p">)</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">48</span><span class="p">,</span><span class="mi">259</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">180</span><span class="p">]</span> <span class="n">Saved</span> <span class="n">to</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span><span class="n">decoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">48</span><span class="p">,</span><span class="mi">259</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">339</span><span class="p">]</span> <span class="n">Exporting</span> <span class="n">joiner</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">48</span><span class="p">,</span><span class="mi">304</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">export</span><span class="o">-</span><span class="k">for</span><span class="o">-</span><span class="n">ncnn</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">207</span><span class="p">]</span> <span class="n">Saved</span> <span class="n">to</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span><span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span>
</pre></div>
</div>
<p>The log shows the model has <code class="docutils literal notranslate"><span class="pre">84176356</span></code> parameters, i.e., <code class="docutils literal notranslate"><span class="pre">~84</span> <span class="pre">M</span></code>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ls</span> <span class="o">-</span><span class="n">lh</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span><span class="n">pretrained</span><span class="o">-</span><span class="nb">iter</span><span class="o">-</span><span class="mi">468000</span><span class="o">-</span><span class="n">avg</span><span class="o">-</span><span class="mf">16.</span><span class="n">pt</span>
<span class="o">-</span><span class="n">rw</span><span class="o">-</span><span class="n">r</span><span class="o">--</span><span class="n">r</span><span class="o">--</span> <span class="mi">1</span> <span class="n">kuangfangjun</span> <span class="n">root</span> <span class="mi">324</span><span class="n">M</span> <span class="n">Feb</span> <span class="mi">17</span> <span class="mi">10</span><span class="p">:</span><span class="mi">34</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span><span class="n">pretrained</span><span class="o">-</span><span class="nb">iter</span><span class="o">-</span><span class="mi">468000</span><span class="o">-</span><span class="n">avg</span><span class="o">-</span><span class="mf">16.</span><span class="n">pt</span>
</pre></div>
</div>
<p>You can see that the file size of the pre-trained model is <code class="docutils literal notranslate"><span class="pre">324</span> <span class="pre">MB</span></code>, which
is roughly equal to <code class="docutils literal notranslate"><span class="pre">84176356*4/1024/1024</span> <span class="pre">=</span> <span class="pre">321.107</span> <span class="pre">MB</span></code>.</p>
</div>
<p>After running <code class="docutils literal notranslate"><span class="pre">lstm_transducer_stateless2/export-for-ncnn.py</span></code>,
we will get the following files:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ls<span class="w"> </span>-lh<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/*pnnx.pt
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>1010K<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:22<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.pt
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>318M<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:22<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.pt
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span><span class="m">3</span>.0M<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:22<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.pt
</pre></div>
</div>
</section>
<section id="export-torchscript-model-via-pnnx">
<span id="lstm-transducer-step-4-export-torchscript-model-via-pnnx"></span><h2>4. Export torchscript model via pnnx<a class="headerlink" href="#export-torchscript-model-via-pnnx" title="Permalink to this heading"></a></h2>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>Make sure you have set up the <code class="docutils literal notranslate"><span class="pre">PATH</span></code> environment variable
in <a class="reference internal" href="export-ncnn-conv-emformer.html#export-for-ncnn-install-ncnn-and-pnnx"><span class="std std-ref">2. Install ncnn and pnnx</span></a>. Otherwise,
it will throw an error saying that <code class="docutils literal notranslate"><span class="pre">pnnx</span></code> could not be found.</p>
</div>
<p>Now, it’s time to export our models to <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> via <code class="docutils literal notranslate"><span class="pre">pnnx</span></code>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cd</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span>
<span class="n">pnnx</span> <span class="o">./</span><span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span>
<span class="n">pnnx</span> <span class="o">./</span><span class="n">decoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span>
<span class="n">pnnx</span> <span class="o">./</span><span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span>
</pre></div>
</div>
<p>It will generate the following files:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ls<span class="w"> </span>-lh<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/*ncnn*<span class="o">{</span>bin,param<span class="o">}</span>
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>503K<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:32<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.bin
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span><span class="m">437</span><span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:32<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.param
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>159M<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:32<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.bin
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>21K<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:32<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.param
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span><span class="m">1</span>.5M<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:33<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.bin
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span><span class="m">488</span><span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:33<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.param
</pre></div>
</div>
<p>There are two types of files:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">param</span></code>: It is a text file containing the model architectures. You can
use a text editor to view its content.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">bin</span></code>: It is a binary file containing the model parameters.</p></li>
</ul>
<p>We compare the file sizes of the models below before and after converting via <code class="docutils literal notranslate"><span class="pre">pnnx</span></code>:</p>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>File name</p></th>
<th class="head"><p>File size</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>encoder_jit_trace-pnnx.pt</p></td>
<td><p>318 MB</p></td>
</tr>
<tr class="row-odd"><td><p>decoder_jit_trace-pnnx.pt</p></td>
<td><p>1010 KB</p></td>
</tr>
<tr class="row-even"><td><p>joiner_jit_trace-pnnx.pt</p></td>
<td><p>3.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p>encoder_jit_trace-pnnx.ncnn.bin</p></td>
<td><p>159 MB</p></td>
</tr>
<tr class="row-even"><td><p>decoder_jit_trace-pnnx.ncnn.bin</p></td>
<td><p>503 KB</p></td>
</tr>
<tr class="row-odd"><td><p>joiner_jit_trace-pnnx.ncnn.bin</p></td>
<td><p>1.5 MB</p></td>
</tr>
</tbody>
</table>
<p>You can see that the file sizes of the models after conversion are about one half
of the models before conversion:</p>
<blockquote>
<div><ul class="simple">
<li><p>encoder: 318 MB vs 159 MB</p></li>
<li><p>decoder: 1010 KB vs 503 KB</p></li>
<li><p>joiner: 3.0 MB vs 1.5 MB</p></li>
</ul>
</div></blockquote>
<p>The reason is that by default <code class="docutils literal notranslate"><span class="pre">pnnx</span></code> converts <code class="docutils literal notranslate"><span class="pre">float32</span></code> parameters
to <code class="docutils literal notranslate"><span class="pre">float16</span></code>. A <code class="docutils literal notranslate"><span class="pre">float32</span></code> parameter occupies 4 bytes, while it is 2 bytes
for <code class="docutils literal notranslate"><span class="pre">float16</span></code>. Thus, it is <code class="docutils literal notranslate"><span class="pre">twice</span> <span class="pre">smaller</span></code> after conversion.</p>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>If you use <code class="docutils literal notranslate"><span class="pre">pnnx</span> <span class="pre">./encoder_jit_trace-pnnx.pt</span> <span class="pre">fp16=0</span></code>, then <code class="docutils literal notranslate"><span class="pre">pnnx</span></code>
won’t convert <code class="docutils literal notranslate"><span class="pre">float32</span></code> to <code class="docutils literal notranslate"><span class="pre">float16</span></code>.</p>
</div>
</section>
<section id="test-the-exported-models-in-icefall">
<h2>5. Test the exported models in icefall<a class="headerlink" href="#test-the-exported-models-in-icefall" title="Permalink to this heading"></a></h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We assume you have set up the environment variable <code class="docutils literal notranslate"><span class="pre">PYTHONPATH</span></code> when
building <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a>.</p>
</div>
<p>Now we have successfully converted our pre-trained model to <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> format.
The generated 6 files are what we need. You can use the following code to
test the converted models:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>./lstm_transducer_stateless2/streaming-ncnn-decode.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--tokens<span class="w"> </span>./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/data/lang_bpe_500/tokens.txt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--encoder-param-filename<span class="w"> </span>./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--encoder-bin-filename<span class="w"> </span>./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--decoder-param-filename<span class="w"> </span>./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--decoder-bin-filename<span class="w"> </span>./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--joiner-param-filename<span class="w"> </span>./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--joiner-bin-filename<span class="w"> </span>./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/test_wavs/1089-134686-0001.wav
</pre></div>
</div>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p><a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> supports only <code class="docutils literal notranslate"><span class="pre">batch</span> <span class="pre">size</span> <span class="pre">==</span> <span class="pre">1</span></code>, so <code class="docutils literal notranslate"><span class="pre">streaming-ncnn-decode.py</span></code> accepts
only 1 wave file as input.</p>
</div>
<p>The output is given below:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">37</span><span class="p">:</span><span class="mi">30</span><span class="p">,</span><span class="mi">861</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">streaming</span><span class="o">-</span><span class="n">ncnn</span><span class="o">-</span><span class="n">decode</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">255</span><span class="p">]</span> <span class="p">{</span><span class="s1">&#39;tokens&#39;</span><span class="p">:</span> <span class="s1">&#39;./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/data/lang_bpe_500/tokens.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;encoder_param_filename&#39;</span><span class="p">:</span> <span class="s1">&#39;./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.param&#39;</span><span class="p">,</span> <span class="s1">&#39;encoder_bin_filename&#39;</span><span class="p">:</span> <span class="s1">&#39;./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.bin&#39;</span><span class="p">,</span> <span class="s1">&#39;decoder_param_filename&#39;</span><span class="p">:</span> <span class="s1">&#39;./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.param&#39;</span><span class="p">,</span> <span class="s1">&#39;decoder_bin_filename&#39;</span><span class="p">:</span> <span class="s1">&#39;./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.bin&#39;</span><span class="p">,</span> <span class="s1">&#39;joiner_param_filename&#39;</span><span class="p">:</span> <span class="s1">&#39;./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.param&#39;</span><span class="p">,</span> <span class="s1">&#39;joiner_bin_filename&#39;</span><span class="p">:</span> <span class="s1">&#39;./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.bin&#39;</span><span class="p">,</span> <span class="s1">&#39;sound_filename&#39;</span><span class="p">:</span> <span class="s1">&#39;./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/test_wavs/1089-134686-0001.wav&#39;</span><span class="p">}</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">37</span><span class="p">:</span><span class="mi">31</span><span class="p">,</span><span class="mi">425</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">streaming</span><span class="o">-</span><span class="n">ncnn</span><span class="o">-</span><span class="n">decode</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">263</span><span class="p">]</span> <span class="n">Constructing</span> <span class="n">Fbank</span> <span class="n">computer</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">37</span><span class="p">:</span><span class="mi">31</span><span class="p">,</span><span class="mi">427</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">streaming</span><span class="o">-</span><span class="n">ncnn</span><span class="o">-</span><span class="n">decode</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="n">Reading</span> <span class="n">sound</span> <span class="n">files</span><span class="p">:</span> <span class="o">./</span><span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</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">wav</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">37</span><span class="p">:</span><span class="mi">31</span><span class="p">,</span><span class="mi">431</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">streaming</span><span class="o">-</span><span class="n">ncnn</span><span class="o">-</span><span class="n">decode</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">271</span><span class="p">]</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">([</span><span class="mi">106000</span><span class="p">])</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">37</span><span class="p">:</span><span class="mi">34</span><span class="p">,</span><span class="mi">115</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">streaming</span><span class="o">-</span><span class="n">ncnn</span><span class="o">-</span><span class="n">decode</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">342</span><span class="p">]</span> <span class="o">./</span><span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</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">wav</span>
<span class="mi">2023</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">17</span> <span class="mi">11</span><span class="p">:</span><span class="mi">37</span><span class="p">:</span><span class="mi">34</span><span class="p">,</span><span class="mi">115</span> <span class="n">INFO</span> <span class="p">[</span><span class="n">streaming</span><span class="o">-</span><span class="n">ncnn</span><span class="o">-</span><span class="n">decode</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">343</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>
</pre></div>
</div>
<p>Congratulations! You have successfully exported a model from PyTorch to <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a>!</p>
</section>
<section id="modify-the-exported-encoder-for-sherpa-ncnn">
<span id="lstm-modify-the-exported-encoder-for-sherpa-ncnn"></span><h2>6. Modify the exported encoder for sherpa-ncnn<a class="headerlink" href="#modify-the-exported-encoder-for-sherpa-ncnn" title="Permalink to this heading"></a></h2>
<p>In order to use the exported models in <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>, we have to modify
<code class="docutils literal notranslate"><span class="pre">encoder_jit_trace-pnnx.ncnn.param</span></code>.</p>
<p>Let us have a look at the first few lines of <code class="docutils literal notranslate"><span class="pre">encoder_jit_trace-pnnx.ncnn.param</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">7767517</span>
<span class="mi">267</span> <span class="mi">379</span>
<span class="n">Input</span> <span class="n">in0</span> <span class="mi">0</span> <span class="mi">1</span> <span class="n">in0</span>
</pre></div>
</div>
<p><strong>Explanation</strong> of the above three lines:</p>
<blockquote>
<div><ol class="arabic simple">
<li><p><code class="docutils literal notranslate"><span class="pre">7767517</span></code>, it is a magic number and should not be changed.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">267</span> <span class="pre">379</span></code>, the first number <code class="docutils literal notranslate"><span class="pre">267</span></code> specifies the number of layers
in this file, while <code class="docutils literal notranslate"><span class="pre">379</span></code> specifies the number of intermediate outputs
of this file</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Input</span> <span class="pre">in0</span> <span class="pre">0</span> <span class="pre">1</span> <span class="pre">in0</span></code>, <code class="docutils literal notranslate"><span class="pre">Input</span></code> is the layer type of this layer; <code class="docutils literal notranslate"><span class="pre">in0</span></code>
is the layer name of this layer; <code class="docutils literal notranslate"><span class="pre">0</span></code> means this layer has no input;
<code class="docutils literal notranslate"><span class="pre">1</span></code> means this layer has one output; <code class="docutils literal notranslate"><span class="pre">in0</span></code> is the output name of
this layer.</p></li>
</ol>
</div></blockquote>
<p>We need to add 1 extra line and also increment the number of layers.
The result looks like below:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="m">7767517</span>
<span class="m">268</span><span class="w"> </span><span class="m">379</span>
SherpaMetaData<span class="w"> </span>sherpa_meta_data1<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="nv">0</span><span class="o">=</span><span class="m">3</span><span class="w"> </span><span class="nv">1</span><span class="o">=</span><span class="m">12</span><span class="w"> </span><span class="nv">2</span><span class="o">=</span><span class="m">512</span><span class="w"> </span><span class="nv">3</span><span class="o">=</span><span class="m">1024</span>
Input<span class="w"> </span>in0<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">1</span><span class="w"> </span>in0
</pre></div>
</div>
<p><strong>Explanation</strong></p>
<blockquote>
<div><ol class="arabic">
<li><p><code class="docutils literal notranslate"><span class="pre">7767517</span></code>, it is still the same</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">268</span> <span class="pre">379</span></code>, we have added an extra layer, so we need to update <code class="docutils literal notranslate"><span class="pre">267</span></code> to <code class="docutils literal notranslate"><span class="pre">268</span></code>.
We don’t need to change <code class="docutils literal notranslate"><span class="pre">379</span></code> since the newly added layer has no inputs or outputs.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span>&#160; <span class="pre">sherpa_meta_data1</span>&#160; <span class="pre">0</span> <span class="pre">0</span> <span class="pre">0=3</span> <span class="pre">1=12</span> <span class="pre">2=512</span> <span class="pre">3=1024</span></code>
This line is newly added. Its explanation is given below:</p>
<blockquote>
<div><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span></code> is the type of this layer. Must be <code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">sherpa_meta_data1</span></code> is the name of this layer. Must be <code class="docutils literal notranslate"><span class="pre">sherpa_meta_data1</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre">0</span></code> means this layer has no inputs or output. Must be <code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre">0</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">0=3</span></code>, 0 is the key and 3 is the value. MUST be <code class="docutils literal notranslate"><span class="pre">0=3</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">1=12</span></code>, 1 is the key and 12 is the value of the
parameter <code class="docutils literal notranslate"><span class="pre">--num-encoder-layers</span></code> that you provided when running
<code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/export-for-ncnn.py</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">2=512</span></code>, 2 is the key and 512 is the value of the
parameter <code class="docutils literal notranslate"><span class="pre">--encoder-dim</span></code> that you provided when running
<code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/export-for-ncnn.py</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">3=1024</span></code>, 3 is the key and 1024 is the value of the
parameter <code class="docutils literal notranslate"><span class="pre">--rnn-hidden-size</span></code> that you provided when running
<code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/export-for-ncnn.py</span></code>.</p></li>
</ul>
<p>For ease of reference, we list the key-value pairs that you need to add
in the following table. If your model has a different setting, please
change the values for <code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span></code> accordingly. Otherwise, you
will be <code class="docutils literal notranslate"><span class="pre">SAD</span></code>.</p>
<blockquote>
<div><table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>key</p></th>
<th class="head"><p>value</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>0</p></td>
<td><p>3 (fixed)</p></td>
</tr>
<tr class="row-odd"><td><p>1</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">--num-encoder-layers</span></code></p></td>
</tr>
<tr class="row-even"><td><p>2</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">--encoder-dim</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>3</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">--rnn-hidden-size</span></code></p></td>
</tr>
</tbody>
</table>
</div></blockquote>
</div></blockquote>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">Input</span> <span class="pre">in0</span> <span class="pre">0</span> <span class="pre">1</span> <span class="pre">in0</span></code>. No need to change it.</p></li>
</ol>
</div></blockquote>
<div class="admonition caution">
<p class="admonition-title">Caution</p>
<p>When you add a new layer <code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span></code>, please remember to update the
number of layers. In our case, update <code class="docutils literal notranslate"><span class="pre">267</span></code> to <code class="docutils literal notranslate"><span class="pre">268</span></code>. Otherwise,
you will be SAD later.</p>
</div>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>After adding the new layer <code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span></code>, you cannot use this model
with <code class="docutils literal notranslate"><span class="pre">streaming-ncnn-decode.py</span></code> anymore since <code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span></code> is
supported only in <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>.</p>
</div>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p><a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> is very flexible. You can add new layers to it just by text-editing
the <code class="docutils literal notranslate"><span class="pre">param</span></code> file! You don’t need to change the <code class="docutils literal notranslate"><span class="pre">bin</span></code> file.</p>
</div>
<p>Now you can use this model in <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>.
Please refer to the following documentation:</p>
<blockquote>
<div><ul class="simple">
<li><p>Linux/macOS/Windows/arm/aarch64: <a class="reference external" href="https://k2-fsa.github.io/sherpa/ncnn/install/index.html">https://k2-fsa.github.io/sherpa/ncnn/install/index.html</a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Android</span></code>: <a class="reference external" href="https://k2-fsa.github.io/sherpa/ncnn/android/index.html">https://k2-fsa.github.io/sherpa/ncnn/android/index.html</a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">iOS</span></code>: <a class="reference external" href="https://k2-fsa.github.io/sherpa/ncnn/ios/index.html">https://k2-fsa.github.io/sherpa/ncnn/ios/index.html</a></p></li>
<li><p>Python: <a class="reference external" href="https://k2-fsa.github.io/sherpa/ncnn/python/index.html">https://k2-fsa.github.io/sherpa/ncnn/python/index.html</a></p></li>
</ul>
</div></blockquote>
<p>We have a list of pre-trained models that have been exported for <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>:</p>
<blockquote>
<div><ul>
<li><p><a class="reference external" href="https://k2-fsa.github.io/sherpa/ncnn/pretrained_models/index.html">https://k2-fsa.github.io/sherpa/ncnn/pretrained_models/index.html</a></p>
<p>You can find more usages there.</p>
</li>
</ul>
</div></blockquote>
</section>
<section id="optional-int8-quantization-with-sherpa-ncnn">
<h2>7. (Optional) int8 quantization with sherpa-ncnn<a class="headerlink" href="#optional-int8-quantization-with-sherpa-ncnn" title="Permalink to this heading"></a></h2>
<p>This step is optional.</p>
<p>In this step, we describe how to quantize our model with <code class="docutils literal notranslate"><span class="pre">int8</span></code>.</p>
<p>Change <a class="reference internal" href="#lstm-transducer-step-4-export-torchscript-model-via-pnnx"><span class="std std-ref">4. Export torchscript model via pnnx</span></a> to
disable <code class="docutils literal notranslate"><span class="pre">fp16</span></code> when using <code class="docutils literal notranslate"><span class="pre">pnnx</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cd</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span>
<span class="n">pnnx</span> <span class="o">./</span><span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span> <span class="n">fp16</span><span class="o">=</span><span class="mi">0</span>
<span class="n">pnnx</span> <span class="o">./</span><span class="n">decoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span>
<span class="n">pnnx</span> <span class="o">./</span><span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span> <span class="n">fp16</span><span class="o">=</span><span class="mi">0</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We add <code class="docutils literal notranslate"><span class="pre">fp16=0</span></code> when exporting the encoder and joiner. <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> does not
support quantizing the decoder model yet. We will update this documentation
once <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> supports it. (Maybe in this year, 2023).</p>
</div>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ls<span class="w"> </span>-lh<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/*_jit_trace-pnnx.ncnn.<span class="o">{</span>param,bin<span class="o">}</span>
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>503K<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:32<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.bin
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span><span class="m">437</span><span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:32<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.param
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>317M<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:54<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.bin
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>21K<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:54<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.param
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span><span class="m">3</span>.0M<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:54<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.bin
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span><span class="m">488</span><span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">11</span>:54<span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.param
</pre></div>
</div>
<p>Let us compare again the file sizes:</p>
<table class="docutils align-default">
<tbody>
<tr class="row-odd"><td><p>File name</p></td>
<td><p>File size</p></td>
</tr>
<tr class="row-even"><td><p>encoder_jit_trace-pnnx.pt</p></td>
<td><p>318 MB</p></td>
</tr>
<tr class="row-odd"><td><p>decoder_jit_trace-pnnx.pt</p></td>
<td><p>1010 KB</p></td>
</tr>
<tr class="row-even"><td><p>joiner_jit_trace-pnnx.pt</p></td>
<td><p>3.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p>encoder_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
<td><p>159 MB</p></td>
</tr>
<tr class="row-even"><td><p>decoder_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
<td><p>503 KB</p></td>
</tr>
<tr class="row-odd"><td><p>joiner_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
<td><p>1.5 MB</p></td>
</tr>
<tr class="row-even"><td><p>encoder_jit_trace-pnnx.ncnn.bin (fp32)</p></td>
<td><p>317 MB</p></td>
</tr>
<tr class="row-odd"><td><p>joiner_jit_trace-pnnx.ncnn.bin (fp32)</p></td>
<td><p>3.0 MB</p></td>
</tr>
</tbody>
</table>
<p>You can see that the file sizes are doubled when we disable <code class="docutils literal notranslate"><span class="pre">fp16</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>You can again use <code class="docutils literal notranslate"><span class="pre">streaming-ncnn-decode.py</span></code> to test the exported models.</p>
</div>
<p>Next, follow <a class="reference internal" href="#lstm-modify-the-exported-encoder-for-sherpa-ncnn"><span class="std std-ref">6. Modify the exported encoder for sherpa-ncnn</span></a>
to modify <code class="docutils literal notranslate"><span class="pre">encoder_jit_trace-pnnx.ncnn.param</span></code>.</p>
<p>Change</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="m">7767517</span>
<span class="m">267</span><span class="w"> </span><span class="m">379</span>
Input<span class="w"> </span>in0<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">1</span><span class="w"> </span>in0
</pre></div>
</div>
<p>to</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="m">7767517</span>
<span class="m">268</span><span class="w"> </span><span class="m">379</span>
SherpaMetaData<span class="w"> </span>sherpa_meta_data1<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="nv">0</span><span class="o">=</span><span class="m">3</span><span class="w"> </span><span class="nv">1</span><span class="o">=</span><span class="m">12</span><span class="w"> </span><span class="nv">2</span><span class="o">=</span><span class="m">512</span><span class="w"> </span><span class="nv">3</span><span class="o">=</span><span class="m">1024</span>
Input<span class="w"> </span>in0<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">1</span><span class="w"> </span>in0
</pre></div>
</div>
<div class="admonition caution">
<p class="admonition-title">Caution</p>
<p>Please follow <a class="reference internal" href="#lstm-modify-the-exported-encoder-for-sherpa-ncnn"><span class="std std-ref">6. Modify the exported encoder for sherpa-ncnn</span></a>
to change the values for <code class="docutils literal notranslate"><span class="pre">SherpaMetaData</span></code> if your model uses a different setting.</p>
</div>
<p>Next, let us compile <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a> since we will quantize our models within
<a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># We will download sherpa-ncnn to $HOME/open-source/</span>
<span class="c1"># You can change it to anywhere you like.</span>
<span class="nb">cd</span><span class="w"> </span><span class="nv">$HOME</span>
mkdir<span class="w"> </span>-p<span class="w"> </span>open-source
<span class="nb">cd</span><span class="w"> </span>open-source
git<span class="w"> </span>clone<span class="w"> </span>https://github.com/k2-fsa/sherpa-ncnn
<span class="nb">cd</span><span class="w"> </span>sherpa-ncnn
mkdir<span class="w"> </span>build
<span class="nb">cd</span><span class="w"> </span>build
cmake<span class="w"> </span>..
make<span class="w"> </span>-j<span class="w"> </span><span class="m">4</span>
./bin/generate-int8-scale-table
<span class="nb">export</span><span class="w"> </span><span class="nv">PATH</span><span class="o">=</span><span class="nv">$HOME</span>/open-source/sherpa-ncnn/build/bin:<span class="nv">$PATH</span>
</pre></div>
</div>
<p>The output of the above commands are:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="o">(</span>py38<span class="o">)</span><span class="w"> </span>kuangfangjun:build$<span class="w"> </span>generate-int8-scale-table
Please<span class="w"> </span>provide<span class="w"> </span><span class="m">10</span><span class="w"> </span>arg.<span class="w"> </span>Currently<span class="w"> </span>given:<span class="w"> </span><span class="m">1</span>
Usage:
generate-int8-scale-table<span class="w"> </span>encoder.param<span class="w"> </span>encoder.bin<span class="w"> </span>decoder.param<span class="w"> </span>decoder.bin<span class="w"> </span>joiner.param<span class="w"> </span>joiner.bin<span class="w"> </span>encoder-scale-table.txt<span class="w"> </span>joiner-scale-table.txt<span class="w"> </span>wave_filenames.txt
Each<span class="w"> </span>line<span class="w"> </span><span class="k">in</span><span class="w"> </span>wave_filenames.txt<span class="w"> </span>is<span class="w"> </span>a<span class="w"> </span>path<span class="w"> </span>to<span class="w"> </span>some<span class="w"> </span>16k<span class="w"> </span>Hz<span class="w"> </span>mono<span class="w"> </span>wave<span class="w"> </span>file.
</pre></div>
</div>
<p>We need to create a file <code class="docutils literal notranslate"><span class="pre">wave_filenames.txt</span></code>, in which we need to put
some calibration wave files. For testing purpose, we put the <code class="docutils literal notranslate"><span class="pre">test_wavs</span></code>
from the pre-trained model repository
<a class="reference external" href="https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03">https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03</a></p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
<span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
cat<span class="w"> </span><span class="s">&lt;&lt;EOF &gt; wave_filenames.txt</span>
<span class="s">../test_wavs/1089-134686-0001.wav</span>
<span class="s">../test_wavs/1221-135766-0001.wav</span>
<span class="s">../test_wavs/1221-135766-0002.wav</span>
<span class="s">EOF</span>
</pre></div>
</div>
<p>Now we can calculate the scales needed for quantization with the calibration data:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
<span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
generate-int8-scale-table<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./decoder_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./decoder_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./encoder-scale-table.txt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./joiner-scale-table.txt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./wave_filenames.txt
</pre></div>
</div>
<p>The output logs are in the following:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Don</span><span class="s1">&#39;t Use GPU. has_gpu: 0, config.use_vulkan_compute: 1</span>
<span class="n">num</span> <span class="n">encoder</span> <span class="n">conv</span> <span class="n">layers</span><span class="p">:</span> <span class="mi">28</span>
<span class="n">num</span> <span class="n">joiner</span> <span class="n">conv</span> <span class="n">layers</span><span class="p">:</span> <span class="mi">3</span>
<span class="n">num</span> <span class="n">files</span><span class="p">:</span> <span class="mi">3</span>
<span class="n">Processing</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">wav</span>
<span class="n">Processing</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">wav</span>
<span class="n">Processing</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">wav</span>
<span class="n">Processing</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">wav</span>
<span class="n">Processing</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">wav</span>
<span class="n">Processing</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">wav</span>
<span class="o">----------</span><span class="n">encoder</span><span class="o">----------</span>
<span class="n">conv_15</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">15.942385</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">15.930708</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">7.972025</span>
<span class="n">conv_16</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">44.978855</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">17.031788</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">7.456645</span>
<span class="n">conv_17</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">17.868437</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">7.830528</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">16.218575</span>
<span class="n">linear_18</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">3.107259</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">1.194808</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">106.293236</span>
<span class="n">linear_19</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.193777</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.634748</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">27.401705</span>
<span class="n">linear_20</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.259933</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.606617</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">48.722160</span>
<span class="n">linear_21</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">5.186600</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.790260</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">26.512129</span>
<span class="n">linear_22</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.759041</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.265832</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">56.050053</span>
<span class="n">linear_23</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">3.931209</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.099090</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">40.979767</span>
<span class="n">linear_24</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.324160</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.215561</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">57.321835</span>
<span class="n">linear_25</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">3.800708</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.599352</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">35.284134</span>
<span class="n">linear_26</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.492444</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.153369</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">40.274391</span>
<span class="n">linear_27</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">3.660161</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.720994</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">46.674126</span>
<span class="n">linear_28</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">9.415265</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.174434</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">40.007133</span>
<span class="n">linear_29</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">4.038418</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.118534</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">40.724262</span>
<span class="n">linear_30</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.072084</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.936867</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">32.259155</span>
<span class="n">linear_31</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">4.342712</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.599489</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">35.282787</span>
<span class="n">linear_32</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">11.340535</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.120308</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">40.701103</span>
<span class="n">linear_33</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">3.846987</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">3.630030</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">34.985939</span>
<span class="n">linear_34</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.686298</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.204571</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">57.607586</span>
<span class="n">linear_35</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">4.904821</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">4.575518</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">27.756420</span>
<span class="n">linear_36</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">11.806659</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.585589</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">49.118401</span>
<span class="n">linear_37</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">6.402340</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.047157</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">25.162680</span>
<span class="n">linear_38</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">11.174589</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">1.923361</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">66.030258</span>
<span class="n">linear_39</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">16.178576</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">7.556058</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">16.807705</span>
<span class="n">linear_40</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">12.901954</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.301267</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">23.956539</span>
<span class="n">linear_41</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">14.839805</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">7.597429</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">16.716181</span>
<span class="n">linear_42</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.178945</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">2.651595</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">47.895699</span>
<span class="o">----------</span><span class="n">joiner</span><span class="o">----------</span>
<span class="n">linear_2</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">24.829245</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">16.627592</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">7.637907</span>
<span class="n">linear_1</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">10.746186</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">5.255032</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">24.167313</span>
<span class="n">linear_3</span> <span class="p">:</span> <span class="nb">max</span> <span class="o">=</span> <span class="mf">1.000000</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mf">0.999756</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">127.031013</span>
<span class="n">ncnn</span> <span class="n">int8</span> <span class="n">calibration</span> <span class="n">table</span> <span class="n">create</span> <span class="n">success</span><span class="p">,</span> <span class="n">best</span> <span class="n">wish</span> <span class="k">for</span> <span class="n">your</span> <span class="n">int8</span> <span class="n">inference</span> <span class="n">has</span> <span class="n">a</span> <span class="n">low</span> <span class="n">accuracy</span> <span class="n">loss</span><span class="o">...</span>\<span class="p">(</span><span class="o">^</span><span class="mi">0</span><span class="o">^</span><span class="p">)</span><span class="o">/..</span><span class="mf">.233</span><span class="o">...</span>
</pre></div>
</div>
<p>It generates the following two files:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ls<span class="w"> </span>-lh<span class="w"> </span>encoder-scale-table.txt<span class="w"> </span>joiner-scale-table.txt
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>345K<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">12</span>:13<span class="w"> </span>encoder-scale-table.txt
-rw-r--r--<span class="w"> </span><span class="m">1</span><span class="w"> </span>kuangfangjun<span class="w"> </span>root<span class="w"> </span>17K<span class="w"> </span>Feb<span class="w"> </span><span class="m">17</span><span class="w"> </span><span class="m">12</span>:13<span class="w"> </span>joiner-scale-table.txt
</pre></div>
</div>
<div class="admonition caution">
<p class="admonition-title">Caution</p>
<p>Definitely, you need more calibration data to compute the scale table.</p>
</div>
<p>Finally, let us use the scale table to quantize our models into <code class="docutils literal notranslate"><span class="pre">int8</span></code>.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ncnn2int8
usage:<span class="w"> </span>ncnn2int8<span class="w"> </span><span class="o">[</span>inparam<span class="o">]</span><span class="w"> </span><span class="o">[</span>inbin<span class="o">]</span><span class="w"> </span><span class="o">[</span>outparam<span class="o">]</span><span class="w"> </span><span class="o">[</span>outbin<span class="o">]</span><span class="w"> </span><span class="o">[</span>calibration<span class="w"> </span>table<span class="o">]</span>
</pre></div>
</div>
<p>First, we quantize the encoder model:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
<span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
ncnn2int8<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.int8.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.int8.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./encoder-scale-table.txt
</pre></div>
</div>
<p>Next, we quantize the joiner model:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ncnn2int8<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.int8.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.int8.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./joiner-scale-table.txt
</pre></div>
</div>
<p>The above two commands generate the following 4 files:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">-</span><span class="n">rw</span><span class="o">-</span><span class="n">r</span><span class="o">--</span><span class="n">r</span><span class="o">--</span> <span class="mi">1</span> <span class="n">kuangfangjun</span> <span class="n">root</span> <span class="mi">218</span><span class="n">M</span> <span class="n">Feb</span> <span class="mi">17</span> <span class="mi">12</span><span class="p">:</span><span class="mi">19</span> <span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">int8</span><span class="o">.</span><span class="n">bin</span>
<span class="o">-</span><span class="n">rw</span><span class="o">-</span><span class="n">r</span><span class="o">--</span><span class="n">r</span><span class="o">--</span> <span class="mi">1</span> <span class="n">kuangfangjun</span> <span class="n">root</span> <span class="mi">21</span><span class="n">K</span> <span class="n">Feb</span> <span class="mi">17</span> <span class="mi">12</span><span class="p">:</span><span class="mi">19</span> <span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">int8</span><span class="o">.</span><span class="n">param</span>
<span class="o">-</span><span class="n">rw</span><span class="o">-</span><span class="n">r</span><span class="o">--</span><span class="n">r</span><span class="o">--</span> <span class="mi">1</span> <span class="n">kuangfangjun</span> <span class="n">root</span> <span class="mi">774</span><span class="n">K</span> <span class="n">Feb</span> <span class="mi">17</span> <span class="mi">12</span><span class="p">:</span><span class="mi">19</span> <span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">int8</span><span class="o">.</span><span class="n">bin</span>
<span class="o">-</span><span class="n">rw</span><span class="o">-</span><span class="n">r</span><span class="o">--</span><span class="n">r</span><span class="o">--</span> <span class="mi">1</span> <span class="n">kuangfangjun</span> <span class="n">root</span> <span class="mi">496</span> <span class="n">Feb</span> <span class="mi">17</span> <span class="mi">12</span><span class="p">:</span><span class="mi">19</span> <span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">int8</span><span class="o">.</span><span class="n">param</span>
</pre></div>
</div>
<p>Congratulations! You have successfully quantized your model from <code class="docutils literal notranslate"><span class="pre">float32</span></code> to <code class="docutils literal notranslate"><span class="pre">int8</span></code>.</p>
<div class="admonition caution">
<p class="admonition-title">Caution</p>
<p><code class="docutils literal notranslate"><span class="pre">ncnn.int8.param</span></code> and <code class="docutils literal notranslate"><span class="pre">ncnn.int8.bin</span></code> must be used in pairs.</p>
<p>You can replace <code class="docutils literal notranslate"><span class="pre">ncnn.param</span></code> and <code class="docutils literal notranslate"><span class="pre">ncnn.bin</span></code> with <code class="docutils literal notranslate"><span class="pre">ncnn.int8.param</span></code>
and <code class="docutils literal notranslate"><span class="pre">ncnn.int8.bin</span></code> in <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a> if you like.</p>
<p>For instance, to use only the <code class="docutils literal notranslate"><span class="pre">int8</span></code> encoder in <code class="docutils literal notranslate"><span class="pre">sherpa-ncnn</span></code>, you can
replace the following invocation:</p>
<blockquote>
<div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cd</span> <span class="n">egs</span><span class="o">/</span><span class="n">librispeech</span><span class="o">/</span><span class="n">ASR</span>
<span class="n">cd</span> <span class="n">icefall</span><span class="o">-</span><span class="n">asr</span><span class="o">-</span><span class="n">librispeech</span><span class="o">-</span><span class="n">lstm</span><span class="o">-</span><span class="n">transducer</span><span class="o">-</span><span class="n">stateless2</span><span class="o">-</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">03</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span>
<span class="n">sherpa</span><span class="o">-</span><span class="n">ncnn</span> \
<span class="o">../</span><span class="n">data</span><span class="o">/</span><span class="n">lang_bpe_500</span><span class="o">/</span><span class="n">tokens</span><span class="o">.</span><span class="n">txt</span> \
<span class="o">./</span><span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">param</span> \
<span class="o">./</span><span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">bin</span> \
<span class="o">./</span><span class="n">decoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">param</span> \
<span class="o">./</span><span class="n">decoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">bin</span> \
<span class="o">./</span><span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">param</span> \
<span class="o">./</span><span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">ncnn</span><span class="o">.</span><span class="n">bin</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">wav</span>
</pre></div>
</div>
</div></blockquote>
<p>with</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>egs/librispeech/ASR
<span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
sherpa-ncnn<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>../data/lang_bpe_500/tokens.txt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.int8.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./encoder_jit_trace-pnnx.ncnn.int8.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./decoder_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./decoder_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.param<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>./joiner_jit_trace-pnnx.ncnn.bin<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>../test_wavs/1089-134686-0001.wav
</pre></div>
</div>
</div></blockquote>
</div>
<p>The following table compares again the file sizes:</p>
<table class="docutils align-default">
<tbody>
<tr class="row-odd"><td><p>File name</p></td>
<td><p>File size</p></td>
</tr>
<tr class="row-even"><td><p>encoder_jit_trace-pnnx.pt</p></td>
<td><p>318 MB</p></td>
</tr>
<tr class="row-odd"><td><p>decoder_jit_trace-pnnx.pt</p></td>
<td><p>1010 KB</p></td>
</tr>
<tr class="row-even"><td><p>joiner_jit_trace-pnnx.pt</p></td>
<td><p>3.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p>encoder_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
<td><p>159 MB</p></td>
</tr>
<tr class="row-even"><td><p>decoder_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
<td><p>503 KB</p></td>
</tr>
<tr class="row-odd"><td><p>joiner_jit_trace-pnnx.ncnn.bin (fp16)</p></td>
<td><p>1.5 MB</p></td>
</tr>
<tr class="row-even"><td><p>encoder_jit_trace-pnnx.ncnn.bin (fp32)</p></td>
<td><p>317 MB</p></td>
</tr>
<tr class="row-odd"><td><p>joiner_jit_trace-pnnx.ncnn.bin (fp32)</p></td>
<td><p>3.0 MB</p></td>
</tr>
<tr class="row-even"><td><p>encoder_jit_trace-pnnx.ncnn.int8.bin</p></td>
<td><p>218 MB</p></td>
</tr>
<tr class="row-odd"><td><p>joiner_jit_trace-pnnx.ncnn.int8.bin</p></td>
<td><p>774 KB</p></td>
</tr>
</tbody>
</table>
<p>You can see that the file size of the joiner model after <code class="docutils literal notranslate"><span class="pre">int8</span></code> quantization
is much smaller. However, the size of the encoder model is even larger than
the <code class="docutils literal notranslate"><span class="pre">fp16</span></code> counterpart. The reason is that <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> currently does not support
quantizing <code class="docutils literal notranslate"><span class="pre">LSTM</span></code> layers into <code class="docutils literal notranslate"><span class="pre">8-bit</span></code>. Please see
<a class="reference external" href="https://github.com/Tencent/ncnn/issues/4532">https://github.com/Tencent/ncnn/issues/4532</a></p>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>Currently, only linear layers and convolutional layers are quantized
with <code class="docutils literal notranslate"><span class="pre">int8</span></code>, so you don’t see an exact <code class="docutils literal notranslate"><span class="pre">4x</span></code> reduction in file sizes.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>You need to test the recognition accuracy after <code class="docutils literal notranslate"><span class="pre">int8</span></code> quantization.</p>
</div>
<p>That’s it! Have fun with <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">sherpa-ncnn</a>!</p>
</section>
</section>
</div>
</div>
<footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
<a href="export-ncnn-conv-emformer.html" class="btn btn-neutral float-left" title="Export ConvEmformer transducer models to ncnn" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
<a href="../recipes/index.html" class="btn btn-neutral float-right" title="Recipes" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
</div>
<hr/>
<div role="contentinfo">
<p>&#169; Copyright 2021, icefall development team.</p>
</div>
Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
<a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
</div>
</div>
</section>
</div>
<script>
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
});
</script>
</body>
</html>
Reference in New Issue View Git Blame Copy Permalink