deploy: 13daf73468da70f5db49c928969fce6c6edc041a

_sources/recipes/Finetune/from_supervised/finetune_zipformer.rst.txt

@@ -0,0 +1,140 @@
+Finetune from a supervised pre-trained Zipformer model
+======================================================
+
+This tutorial shows you how to fine-tune a supervised pre-trained **Zipformer**
+transducer model on a new dataset.
+
+.. HINT::
+
+  We assume you have read the page :ref:`install icefall` and have setup
+  the environment for ``icefall``.
+
+.. HINT::
+
+  We recommend you to use a GPU or several GPUs to run this recipe
+
+
+For illustration purpose, we fine-tune the Zipformer transducer model
+pre-trained on `LibriSpeech`_ on the small subset of `GigaSpeech`_. You could use your
+own data for fine-tuning if you create a manifest for your new dataset.
+
+Data preparation
+----------------
+
+Please follow the instructions in the `GigaSpeech recipe <https://github.com/k2-fsa/icefall/tree/master/egs/gigaspeech/ASR>`_
+to prepare the fine-tune data used in this tutorial. We only require the small subset in GigaSpeech for this tutorial.
+
+
+Model preparation
+-----------------
+
+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:
+
+.. code-block:: bash
+
+    $ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
+    $ cd icefall-asr-librispeech-zipformer-2023-05-15/exp
+    $ git lfs pull --include "pretrained.pt"
+    $ ln -s pretrained.pt epoch-99.pt
+    $ cd ../data/lang_bpe_500
+    $ git lfs pull --include bpe.model
+    $ cd ../../..
+
+Before fine-tuning, let's test the model's WER on the new domain. The following command performs
+decoding on the GigaSpeech test sets:
+
+.. code-block:: bash
+
+    ./zipformer/decode_gigaspeech.py \
+        --epoch 99 \
+        --avg 1 \
+        --exp-dir icefall-asr-librispeech-zipformer-2023-05-15/exp \
+        --use-averaged-model 0 \
+        --max-duration 1000 \
+        --decoding-method greedy_search
+
+You should see the following numbers:
+
+.. code-block::
+
+    For dev, WER of different settings are:
+    greedy_search	20.06	best for dev
+
+    For test, WER of different settings are:
+    greedy_search	19.27	best for test
+
+
+Fine-tune
+---------
+
+Since LibriSpeech and GigaSpeech are both English dataset, we can initialize the whole
+Zipformer model with the checkpoint downloaded in the previous step (otherwise we should consider
+initializing the stateless decoder and joiner from scratch due to the mismatch of the output
+vocabulary). The following command starts a fine-tuning experiment:
+
+.. code-block:: bash
+
+    $ use_mux=0
+    $ do_finetune=1
+
+    $ ./zipformer/finetune.py \
+        --world-size 2 \
+        --num-epochs 20 \
+        --start-epoch 1 \
+        --exp-dir zipformer/exp_giga_finetune${do_finetune}_mux${use_mux} \
+        --use-fp16 1 \
+        --base-lr 0.0045 \
+        --bpe-model data/lang_bpe_500/bpe.model \
+        --do-finetune $do_finetune \
+        --use-mux $use_mux \
+        --master-port 13024 \
+        --finetune-ckpt icefall-asr-librispeech-zipformer-2023-05-15/exp/pretrained.pt \
+        --max-duration 1000
+
+The following arguments are related to fine-tuning:
+
+- ``--base-lr``
+    The learning rate used for fine-tuning. We suggest to set a **small** learning rate for fine-tuning,
+    otherwise the model may forget the initialization very quickly. A reasonable value should be around
+    1/10 of the original lr, i.e 0.0045.
+
+- ``--do-finetune``
+    If True, do fine-tuning by initializing the model from a pre-trained checkpoint.
+    **Note that if you want to resume your fine-tuning experiment from certain epochs, you
+    need to set this to False.**
+
+- ``--finetune-ckpt``
+    The path to the pre-trained checkpoint (used for initialization).
+
+- ``--use-mux``
+    If True, mix the fine-tune data with the original training data by using `CutSet.mux <https://lhotse.readthedocs.io/en/latest/api.html#lhotse.supervision.SupervisionSet.mux>`_
+    This helps maintain the model's performance on the original domain if the original training
+    is available. **If you don't have the original training data, please set it to False.**
+
+After fine-tuning, let's test the WERs. You can do this via the following command:
+
+.. code-block:: bash
+
+    $ use_mux=0
+    $ do_finetune=1
+    $ ./zipformer/decode_gigaspeech.py \
+        --epoch 20 \
+        --avg 10 \
+        --exp-dir zipformer/exp_giga_finetune${do_finetune}_mux${use_mux} \
+        --use-averaged-model 1 \
+        --max-duration 1000 \
+        --decoding-method greedy_search
+
+You should see numbers similar to the ones below:
+
+.. code-block:: text
+
+    For dev, WER of different settings are:
+    greedy_search	13.47	best for dev
+
+    For test, WER of different settings are:
+    greedy_search	13.66	best for test
+
+Compared to the original checkpoint, the fine-tuned model achieves much lower WERs
+on the GigaSpeech test sets.

_sources/recipes/Finetune/index.rst.txt

@@ -0,0 +1,15 @@
+Fine-tune a pre-trained model
+=============================
+
+After pre-training on public available datasets, the ASR model is already capable of
+performing general speech recognition with relatively high accuracy. However, the accuracy
+could be still low on certain domains that are quite different from the original training
+set. In this case, we can fine-tune the model with a small amount of additional labelled
+data to improve the performance on new domains.
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Table of Contents
+
+   from_supervised/finetune_zipformer

_sources/recipes/index.rst.txt

@@ -17,3 +17,4 @@ We may add recipes for other tasks as well in the future.
    Streaming-ASR/index
    RNN-LM/index
    TTS/index
+   Finetune/index

contributing/index.html

@@ -22,7 +22,7 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="Contributing to Documentation" href="doc.html" />
-    <link rel="prev" title="VITS" href="../recipes/TTS/vctk/vits.html" /> 
+    <link rel="prev" title="Finetune from a supervised pre-trained Zipformer model" href="../recipes/Finetune/from_supervised/finetune_zipformer.html" /> 
 </head>
 
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@@ -135,7 +135,7 @@ and code to <code class="docutils literal notranslate"><span class="pre">icefall
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           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="../recipes/TTS/vctk/vits.html" class="btn btn-neutral float-left" title="VITS" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../recipes/Finetune/from_supervised/finetune_zipformer.html" class="btn btn-neutral float-left" title="Finetune from a supervised pre-trained Zipformer model" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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index.html

@@ -163,6 +163,10 @@ speech recognition recipes using <a class="reference external" href="https://git
 <li class="toctree-l3"><a class="reference internal" href="recipes/TTS/vctk/vits.html">VITS</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="recipes/Finetune/index.html">Fine-tune a pre-trained model</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="recipes/Finetune/from_supervised/finetune_zipformer.html">Finetune from a supervised pre-trained Zipformer model</a></li>
+</ul>
+</li>
 </ul>
 </li>
 </ul>

recipes/Finetune/from_supervised/finetune_zipformer.html

@@ -0,0 +1,270 @@
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+        </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>
+<li class="toctree-l1"><a class="reference internal" href="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
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+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
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+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Fine-tune a pre-trained model</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Finetune from a supervised pre-trained Zipformer model</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#data-preparation">Data preparation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#model-preparation">Model preparation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#fine-tune">Fine-tune</a></li>
+</ul>
+</li>
+</ul>
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+           <div itemprop="articleBody">
+             
+  <section id="finetune-from-a-supervised-pre-trained-zipformer-model">
+<h1>Finetune from a supervised pre-trained Zipformer model<a class="headerlink" href="#finetune-from-a-supervised-pre-trained-zipformer-model" title="Permalink to this heading"></a></h1>
+<p>This tutorial shows you how to fine-tune a supervised pre-trained <strong>Zipformer</strong>
+transducer model on a new dataset.</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">
+<h2>Fine-tune<a class="headerlink" href="#fine-tune" title="Permalink to this heading"></a></h2>
+<p>Since LibriSpeech and GigaSpeech are both English dataset, we can initialize the whole
+Zipformer model with the checkpoint downloaded in the previous step (otherwise we should consider
+initializing the stateless decoder and joiner from scratch due to the mismatch of the output
+vocabulary). The following command starts a fine-tuning experiment:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">use_mux</span><span class="o">=</span><span class="m">0</span>
+$<span class="w"> </span><span class="nv">do_finetune</span><span class="o">=</span><span class="m">1</span>
+
+$<span class="w"> </span>./zipformer/finetune.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/exp_giga_finetune<span class="si">${</span><span class="nv">do_finetune</span><span class="si">}</span>_mux<span class="si">${</span><span class="nv">use_mux</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>.0045<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">$do_finetune</span><span class="w"> </span><span class="se">\</span>
+<span class="w">    </span>--use-mux<span class="w"> </span><span class="nv">$use_mux</span><span class="w"> </span><span class="se">\</span>
+<span class="w">    </span>--master-port<span class="w"> </span><span class="m">13024</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">--base-lr</span></code></dt><dd><p>The learning rate used for fine-tuning. We suggest to set a <strong>small</strong> learning rate for fine-tuning,
+otherwise the model may forget the initialization very quickly. A reasonable value should be around
+1/10 of the original lr, i.e 0.0045.</p>
+</dd>
+</dl>
+</li>
+<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">--finetune-ckpt</span></code></dt><dd><p>The path to the pre-trained checkpoint (used for initialization).</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt><code class="docutils literal notranslate"><span class="pre">--use-mux</span></code></dt><dd><p>If True, mix the fine-tune data with the original training data by using <a class="reference external" href="https://lhotse.readthedocs.io/en/latest/api.html#lhotse.supervision.SupervisionSet.mux">CutSet.mux</a>
+This helps maintain the model’s performance on the original domain if the original training
+is available. <strong>If you don’t have the original training data, please set it to False.</strong></p>
+</dd>
+</dl>
+</li>
+</ul>
+<p>After fine-tuning, let’s test the WERs. You can do this via the following command:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">use_mux</span><span class="o">=</span><span class="m">0</span>
+$<span class="w"> </span><span class="nv">do_finetune</span><span class="o">=</span><span class="m">1</span>
+$<span class="w"> </span>./zipformer/decode_gigaspeech.py<span class="w"> </span><span class="se">\</span>
+<span class="w">    </span>--epoch<span class="w"> </span><span class="m">20</span><span class="w"> </span><span class="se">\</span>
+<span class="w">    </span>--avg<span class="w"> </span><span class="m">10</span><span class="w"> </span><span class="se">\</span>
+<span class="w">    </span>--exp-dir<span class="w"> </span>zipformer/exp_giga_finetune<span class="si">${</span><span class="nv">do_finetune</span><span class="si">}</span>_mux<span class="si">${</span><span class="nv">use_mux</span><span class="si">}</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>--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 numbers similar to the ones below:</p>
+<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>For dev, WER of different settings are:
+greedy_search       13.47   best for dev
+
+For test, WER of different settings are:
+greedy_search       13.66   best for test
+</pre></div>
+</div>
+<p>Compared to the original checkpoint, the fine-tuned model achieves much lower WERs
+on the GigaSpeech test sets.</p>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../index.html" class="btn btn-neutral float-left" title="Fine-tune a pre-trained model" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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recipes/Finetune/index.html

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+  <title>Fine-tune a pre-trained model &mdash; icefall 0.1 documentation</title>
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+    <link rel="next" title="Finetune from a supervised pre-trained Zipformer model" href="from_supervised/finetune_zipformer.html" />
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+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../docker/index.html">Docker</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
+</ul>
+<ul class="current">
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../Non-streaming-ASR/index.html">Non Streaming ASR</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Streaming-ASR/index.html">Streaming ASR</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../RNN-LM/index.html">RNN-LM</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../TTS/index.html">TTS</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Fine-tune a pre-trained model</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="from_supervised/finetune_zipformer.html">Finetune from a supervised pre-trained Zipformer model</a></li>
+</ul>
+</li>
+</ul>
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+<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>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../decoding-with-langugage-models/index.html">Decoding with language models</a></li>
+</ul>
+
+        </div>
+      </div>
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+      <li class="breadcrumb-item active">Fine-tune a pre-trained model</li>
+      <li class="wy-breadcrumbs-aside">
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Finetune/index.rst" class="fa fa-github"> Edit on GitHub</a>
+      </li>
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+  <hr/>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="fine-tune-a-pre-trained-model">
+<h1>Fine-tune a pre-trained model<a class="headerlink" href="#fine-tune-a-pre-trained-model" title="Permalink to this heading"></a></h1>
+<p>After pre-training on public available datasets, the ASR model is already capable of
+performing general speech recognition with relatively high accuracy. However, the accuracy
+could be still low on certain domains that are quite different from the original training
+set. In this case, we can fine-tune the model with a small amount of additional labelled
+data to improve the performance on new domains.</p>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">Table of Contents</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="from_supervised/finetune_zipformer.html">Finetune from a supervised pre-trained Zipformer model</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="from_supervised/finetune_zipformer.html#data-preparation">Data preparation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="from_supervised/finetune_zipformer.html#model-preparation">Model preparation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="from_supervised/finetune_zipformer.html#fine-tune">Fine-tune</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../TTS/vctk/vits.html" class="btn btn-neutral float-left" title="VITS" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="from_supervised/finetune_zipformer.html" class="btn btn-neutral float-right" title="Finetune from a supervised pre-trained Zipformer model" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
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+    <p>&#169; Copyright 2021, icefall development team.</p>
+  </div>
+
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+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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+          SphinxRtdTheme.Navigation.enable(true);
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+
+</body>
+</html>

recipes/Non-streaming-ASR/aishell/conformer_ctc.html

@@ -69,6 +69,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/aishell/index.html

@@ -69,6 +69,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/aishell/stateless_transducer.html

@@ -69,6 +69,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/aishell/tdnn_lstm_ctc.html

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 <li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/index.html

@@ -64,6 +64,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../Streaming-ASR/index.html">Streaming ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/librispeech/conformer_ctc.html

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 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/librispeech/distillation.html

@@ -72,6 +72,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/librispeech/index.html

@@ -72,6 +72,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/librispeech/pruned_transducer_stateless.html

@@ -72,6 +72,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/librispeech/tdnn_lstm_ctc.html

@@ -72,6 +72,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/librispeech/zipformer_ctc_blankskip.html

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 <li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/librispeech/zipformer_mmi.html

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 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/timit/index.html

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 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/timit/tdnn_ligru_ctc.html

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 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/timit/tdnn_lstm_ctc.html

@@ -68,6 +68,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/yesno/index.html

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 <li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Non-streaming-ASR/yesno/tdnn.html

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 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/RNN-LM/index.html

@@ -61,6 +61,7 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/RNN-LM/librispeech/lm-training.html

@@ -61,6 +61,7 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>

recipes/Streaming-ASR/index.html

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 <li class="toctree-l2"><a class="reference internal" href="../RNN-LM/index.html">RNN-LM</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Finetune/index.html">Fine-tune a pre-trained model</a></li>
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recipes/Streaming-ASR/introduction.html

@@ -66,6 +66,7 @@
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+<li class="toctree-l2"><a class="reference internal" href="../Finetune/index.html">Fine-tune a pre-trained model</a></li>
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recipes/Streaming-ASR/librispeech/index.html

@@ -67,6 +67,7 @@
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 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
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recipes/Streaming-ASR/librispeech/lstm_pruned_stateless_transducer.html

@@ -67,6 +67,7 @@
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+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
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recipes/Streaming-ASR/librispeech/pruned_transducer_stateless.html

@@ -67,6 +67,7 @@
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 <li class="toctree-l2"><a class="reference internal" href="../../TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
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recipes/Streaming-ASR/librispeech/zipformer_transducer.html

@@ -67,6 +67,7 @@
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+<li class="toctree-l2"><a class="reference internal" href="../../Finetune/index.html">Fine-tune a pre-trained model</a></li>
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recipes/TTS/index.html

@@ -62,6 +62,7 @@
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recipes/TTS/ljspeech/vits.html

@@ -70,6 +70,7 @@
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recipes/TTS/vctk/vits.html

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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="Contributing" href="../../../contributing/index.html" />
+    <link rel="next" title="Fine-tune a pre-trained model" href="../../Finetune/index.html" />
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 </head>
 
@@ -70,6 +70,7 @@
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 </li>
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 </ul>
 </li>
 </ul>
@@ -219,7 +220,7 @@ by visiting the following link:</p>
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           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
         <a href="../ljspeech/vits.html" class="btn btn-neutral float-left" title="VITS" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="../../../contributing/index.html" class="btn btn-neutral float-right" title="Contributing" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="../../Finetune/index.html" class="btn btn-neutral float-right" title="Fine-tune a pre-trained model" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>

recipes/index.html

@@ -58,6 +58,7 @@
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 <li class="toctree-l2"><a class="reference internal" href="TTS/index.html">TTS</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Finetune/index.html">Fine-tune a pre-trained model</a></li>
 </ul>
 </li>
 </ul>
@@ -122,6 +123,10 @@ Currently, we provide recipes for speech recognition, language model, and speech
 <li class="toctree-l2"><a class="reference internal" href="TTS/vctk/vits.html">VITS</a></li>
 </ul>
 </li>
+<li class="toctree-l1"><a class="reference internal" href="Finetune/index.html">Fine-tune a pre-trained model</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="Finetune/from_supervised/finetune_zipformer.html">Finetune from a supervised pre-trained Zipformer model</a></li>
+</ul>
+</li>
 </ul>
 </div>
 </section>