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<section id="finetune-from-a-pre-trained-zipformer-model-with-adapters">
<h1>Finetune from a pre-trained Zipformer model with adapters<a class="headerlink" href="#finetune-from-a-pre-trained-zipformer-model-with-adapters" title="Permalink to this heading"></a></h1>
<p>This tutorial shows you how to fine-tune a pre-trained <strong>Zipformer</strong>
transducer model on a new dataset with adapters.
Adapters are compact and efficient module that can be integrated into a pre-trained model
to improve the model’s performance on a new domain. Adapters are injected
between different modules in the well-trained neural network. During training, only the parameters
in the adapters will be updated. It achieves competitive performance
while requiring much less GPU memory than full fine-tuning. For more details about adapters,
please refer to the original <a class="reference external" href="https://arxiv.org/pdf/1902.00751.pdf#/">paper</a> for more details.</p>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
</div>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>We recommend you to use a GPU or several GPUs to run this recipe</p>
</div>
<p>For illustration purpose, we fine-tune the Zipformer transducer model
pre-trained on <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a> on the small subset of <a class="reference external" href="https://github.com/SpeechColab/GigaSpeech">GigaSpeech</a>. You could use your
own data for fine-tuning if you create a manifest for your new dataset.</p>
<section id="data-preparation">
<h2>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this heading"></a></h2>
<p>Please follow the instructions in the <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/gigaspeech/ASR">GigaSpeech recipe</a>
to prepare the fine-tune data used in this tutorial. We only require the small subset in GigaSpeech for this tutorial.</p>
</section>
<section id="model-preparation">
<h2>Model preparation<a class="headerlink" href="#model-preparation" title="Permalink to this heading"></a></h2>
<p>We are using the Zipformer model trained on full LibriSpeech (960 hours) as the intialization. The
checkpoint of the model can be downloaded via the following command:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><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/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-zipformer-2023-05-15/exp
$<span class="w"> </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;pretrained.pt&quot;</span>
$<span class="w"> </span>ln<span class="w"> </span>-s<span class="w"> </span>pretrained.pt<span class="w"> </span>epoch-99.pt
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>../data/lang_bpe_500
$<span class="w"> </span>git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span>bpe.model
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>../../..
</pre></div>
</div>
<p>Before fine-tuning, let’s test the model’s WER on the new domain. The following command performs
decoding on the GigaSpeech test sets:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./zipformer/decode_gigaspeech.py<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>--exp-dir<span class="w"> </span>icefall-asr-librispeech-zipformer-2023-05-15/exp<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>--max-duration<span class="w"> </span><span class="m">1000</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--decoding-method<span class="w"> </span>greedy_search
</pre></div>
</div>
<p>You should see the following numbers:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">For</span> <span class="n">dev</span><span class="p">,</span> <span class="n">WER</span> <span class="n">of</span> <span class="n">different</span> <span class="n">settings</span> <span class="n">are</span><span class="p">:</span>
<span class="n">greedy_search</span> <span class="mf">20.06</span> <span class="n">best</span> <span class="k">for</span> <span class="n">dev</span>
<span class="n">For</span> <span class="n">test</span><span class="p">,</span> <span class="n">WER</span> <span class="n">of</span> <span class="n">different</span> <span class="n">settings</span> <span class="n">are</span><span class="p">:</span>
<span class="n">greedy_search</span> <span class="mf">19.27</span> <span class="n">best</span> <span class="k">for</span> <span class="n">test</span>
</pre></div>
</div>
</section>
<section id="fine-tune-with-adapter">
<h2>Fine-tune with adapter<a class="headerlink" href="#fine-tune-with-adapter" title="Permalink to this heading"></a></h2>
<p>We insert 4 adapters with residual connection in each <code class="docutils literal notranslate"><span class="pre">Zipformer2EncoderLayer</span></code>.
The original model parameters remain untouched during training and only the parameters of
the adapters are updated. The following command starts a fine-tuning experiment with adapters:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">do_finetune</span><span class="o">=</span><span class="m">1</span>
$<span class="w"> </span><span class="nv">use_adapters</span><span class="o">=</span><span class="m">1</span>
$<span class="w"> </span><span class="nv">adapter_dim</span><span class="o">=</span><span class="m">8</span>
$<span class="w"> </span>./zipformer_adapter/train.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--world-size<span class="w"> </span><span class="m">2</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--num-epochs<span class="w"> </span><span class="m">20</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--start-epoch<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--exp-dir<span class="w"> </span>zipformer_adapter/exp_giga_finetune_adapters<span class="si">${</span><span class="nv">use_adapters</span><span class="si">}</span>_adapter_dim<span class="si">${</span><span class="nv">adapter_dim</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-fp16<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--base-lr<span class="w"> </span><span class="m">0</span>.045<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-adapters<span class="w"> </span><span class="nv">$use_adapters</span><span class="w"> </span>--adapter-dim<span class="w"> </span><span class="nv">$adapter_dim</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--bpe-model<span class="w"> </span>data/lang_bpe_500/bpe.model<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--do-finetune<span class="w"> </span><span class="nv">$do_finetune</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--master-port<span class="w"> </span><span class="m">13022</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--finetune-ckpt<span class="w"> </span>icefall-asr-librispeech-zipformer-2023-05-15/exp/pretrained.pt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">1000</span>
</pre></div>
</div>
<p>The following arguments are related to fine-tuning:</p>
<ul class="simple">
<li><dl class="simple">
<dt><code class="docutils literal notranslate"><span class="pre">--do-finetune</span></code></dt><dd><p>If True, do fine-tuning by initializing the model from a pre-trained checkpoint.
<strong>Note that if you want to resume your fine-tuning experiment from certain epochs, you
need to set this to False.</strong></p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt><code class="docutils literal notranslate"><span class="pre">use-adapters</span></code></dt><dd><p>If adapters are used during fine-tuning.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt><code class="docutils literal notranslate"><span class="pre">--adapter-dim</span></code></dt><dd><p>The bottleneck dimension of the adapter module. Typically a small number.</p>
</dd>
</dl>
</li>
</ul>
<p>You should notice that in the training log, the total number of trainale parameters is shown:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">2024</span><span class="o">-</span><span class="mi">02</span><span class="o">-</span><span class="mi">22</span> <span class="mi">21</span><span class="p">:</span><span class="mi">22</span><span class="p">:</span><span class="mi">03</span><span class="p">,</span><span class="mi">808</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">1277</span><span class="p">]</span> <span class="n">A</span> <span class="n">total</span> <span class="n">of</span> <span class="mi">761344</span> <span class="n">trainable</span> <span class="n">parameters</span> <span class="p">(</span><span class="mf">1.148</span><span class="o">%</span> <span class="n">of</span> <span class="n">the</span> <span class="n">whole</span> <span class="n">model</span><span class="p">)</span>
</pre></div>
</div>
<p>The trainable parameters only makes up 1.15% of the entire model parameters, so the training will be much faster
and requires less memory than full fine-tuning.</p>
</section>
<section id="decoding">
<h2>Decoding<a class="headerlink" href="#decoding" title="Permalink to this heading"></a></h2>
<p>After training, let’s test the WERs. To test the WERs on the GigaSpeech set,
you can execute the following command:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">epoch</span><span class="o">=</span><span class="m">20</span>
$<span class="w"> </span><span class="nv">avg</span><span class="o">=</span><span class="m">10</span>
$<span class="w"> </span><span class="nv">use_adapters</span><span class="o">=</span><span class="m">1</span>
$<span class="w"> </span><span class="nv">adapter_dim</span><span class="o">=</span><span class="m">8</span>
%<span class="w"> </span>./zipformer/decode.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--epoch<span class="w"> </span><span class="nv">$epoch</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--avg<span class="w"> </span><span class="nv">$avg</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-averaged-model<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--exp-dir<span class="w"> </span>zipformer_adapter/exp_giga_finetune_adapters<span class="si">${</span><span class="nv">use_adapters</span><span class="si">}</span>_adapter_dim<span class="si">${</span><span class="nv">adapter_dim</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">600</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-adapters<span class="w"> </span><span class="nv">$use_adapters</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--adapter-dim<span class="w"> </span><span class="nv">$adapter_dim</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--decoding-method<span class="w"> </span>greedy_search
</pre></div>
</div>
<p>You should see the following numbers:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">For</span> <span class="n">dev</span><span class="p">,</span> <span class="n">WER</span> <span class="n">of</span> <span class="n">different</span> <span class="n">settings</span> <span class="n">are</span><span class="p">:</span>
<span class="n">greedy_search</span> <span class="mf">15.44</span> <span class="n">best</span> <span class="k">for</span> <span class="n">dev</span>
<span class="n">For</span> <span class="n">test</span><span class="p">,</span> <span class="n">WER</span> <span class="n">of</span> <span class="n">different</span> <span class="n">settings</span> <span class="n">are</span><span class="p">:</span>
<span class="n">greedy_search</span> <span class="mf">15.42</span> <span class="n">best</span> <span class="k">for</span> <span class="n">test</span>
</pre></div>
</div>
<p>The WER on test set is improved from 19.27 to 15.42, demonstrating the effectiveness of adapters.</p>
<p>The same model can be used to perform decoding on LibriSpeech test sets. You can deactivate the adapters
to keep the same performance of the original model:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">epoch</span><span class="o">=</span><span class="m">20</span>
$<span class="w"> </span><span class="nv">avg</span><span class="o">=</span><span class="m">1</span>
$<span class="w"> </span><span class="nv">use_adapters</span><span class="o">=</span><span class="m">0</span>
$<span class="w"> </span><span class="nv">adapter_dim</span><span class="o">=</span><span class="m">8</span>
%<span class="w"> </span>./zipformer/decode.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--epoch<span class="w"> </span><span class="nv">$epoch</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--avg<span class="w"> </span><span class="nv">$avg</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-averaged-model<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--exp-dir<span class="w"> </span>zipformer_adapter/exp_giga_finetune_adapters<span class="si">${</span><span class="nv">use_adapters</span><span class="si">}</span>_adapter_dim<span class="si">${</span><span class="nv">adapter_dim</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">600</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-adapters<span class="w"> </span><span class="nv">$use_adapters</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--adapter-dim<span class="w"> </span><span class="nv">$adapter_dim</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--decoding-method<span class="w"> </span>greedy_search
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">For</span> <span class="n">dev</span><span class="p">,</span> <span class="n">WER</span> <span class="n">of</span> <span class="n">different</span> <span class="n">settings</span> <span class="n">are</span><span class="p">:</span>
<span class="n">greedy_search</span> <span class="mf">2.23</span> <span class="n">best</span> <span class="k">for</span> <span class="n">test</span><span class="o">-</span><span class="n">clean</span>
<span class="n">For</span> <span class="n">test</span><span class="p">,</span> <span class="n">WER</span> <span class="n">of</span> <span class="n">different</span> <span class="n">settings</span> <span class="n">are</span><span class="p">:</span>
<span class="n">greedy_search</span> <span class="mf">4.96</span> <span class="n">best</span> <span class="k">for</span> <span class="n">test</span><span class="o">-</span><span class="n">other</span>
</pre></div>
</div>
<p>The numbers are the same as reported in <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md#normal-scaled-model-number-of-model-parameters-65549011-ie-6555-m">icefall</a>. So adapter-based
fine-tuning is also very flexible as the same model can be used for decoding on the original and target domain.</p>
</section>
<section id="export-the-model">
<h2>Export the model<a class="headerlink" href="#export-the-model" title="Permalink to this heading"></a></h2>
<p>After training, the model can be exported to <code class="docutils literal notranslate"><span class="pre">onnx</span></code> format easily using the following command:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">use_adapters</span><span class="o">=</span><span class="m">1</span>
$<span class="w"> </span><span class="nv">adapter_dim</span><span class="o">=</span><span class="m">16</span>
$<span class="w"> </span>./zipformer_adapter/export-onnx.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--tokens<span class="w"> </span>icefall-asr-librispeech-zipformer-2023-05-15/data/lang_bpe_500/tokens.txt<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-averaged-model<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--epoch<span class="w"> </span><span class="m">20</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--avg<span class="w"> </span><span class="m">10</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--exp-dir<span class="w"> </span>zipformer_adapter/exp_giga_finetune_adapters<span class="si">${</span><span class="nv">use_adapters</span><span class="si">}</span>_adapter_dim<span class="si">${</span><span class="nv">adapter_dim</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--use-adapters<span class="w"> </span><span class="nv">$use_adapters</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--adapter-dim<span class="w"> </span><span class="nv">$adapter_dim</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--num-encoder-layers<span class="w"> </span><span class="s2">&quot;2,2,3,4,3,2&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--downsampling-factor<span class="w"> </span><span class="s2">&quot;1,2,4,8,4,2&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--feedforward-dim<span class="w"> </span><span class="s2">&quot;512,768,1024,1536,1024,768&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--num-heads<span class="w"> </span><span class="s2">&quot;4,4,4,8,4,4&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--encoder-dim<span class="w"> </span><span class="s2">&quot;192,256,384,512,384,256&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--query-head-dim<span class="w"> </span><span class="m">32</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--value-head-dim<span class="w"> </span><span class="m">12</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--pos-head-dim<span class="w"> </span><span class="m">4</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--pos-dim<span class="w"> </span><span class="m">48</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--encoder-unmasked-dim<span class="w"> </span><span class="s2">&quot;192,192,256,256,256,192&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--cnn-module-kernel<span class="w"> </span><span class="s2">&quot;31,31,15,15,15,31&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--decoder-dim<span class="w"> </span><span class="m">512</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--joiner-dim<span class="w"> </span><span class="m">512</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--causal<span class="w"> </span>False<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--chunk-size<span class="w"> </span><span class="s2">&quot;16,32,64,-1&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--left-context-frames<span class="w"> </span><span class="s2">&quot;64,128,256,-1&quot;</span>
</pre></div>
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