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_sources/recipes/TTS/index.rst.txt Normal file
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TTS
======
.. toctree::
:maxdepth: 2
ljspeech/vits

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_sources/recipes/TTS/ljspeech/vits.rst.txt Normal file
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VITS
===============
This tutorial shows you how to train an VITS model
with the `LJSpeech <https://keithito.com/LJ-Speech-Dataset/>`_ dataset.
.. note::
The VITS paper: `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech <https://arxiv.org/pdf/2106.06103.pdf>`_
Data preparation
----------------
.. code-block:: bash
$ cd egs/ljspeech/TTS
$ ./prepare.sh
To run stage 1 to stage 5, use
.. code-block:: bash
$ ./prepare.sh --stage 1 --stop_stage 5
Build Monotonic Alignment Search
--------------------------------
.. code-block:: bash
$ cd vits/monotonic_align
$ python setup.py build_ext --inplace
$ cd ../../
Training
--------
.. code-block:: bash
$ export CUDA_VISIBLE_DEVICES="0,1,2,3"
$ ./vits/train.py \
--world-size 4 \
--num-epochs 1000 \
--start-epoch 1 \
--use-fp16 1 \
--exp-dir vits/exp \
--tokens data/tokens.txt
--max-duration 500
.. note::
You can adjust the hyper-parameters to control the size of the VITS model and
the training configurations. For more details, please run ``./vits/train.py --help``.
.. note::
The training can take a long time (usually a couple of days).
Training logs, checkpoints and tensorboard logs are saved in ``vits/exp``.
Inference
---------
The inference part uses checkpoints saved by the training part, so you have to run the
training part first. It will save the ground-truth and generated wavs to the directory
``vits/exp/infer/epoch-*/wav``, e.g., ``vits/exp/infer/epoch-1000/wav``.
.. code-block:: bash
$ export CUDA_VISIBLE_DEVICES="0"
$ ./vits/infer.py \
--epoch 1000 \
--exp-dir vits/exp \
--tokens data/tokens.txt
--max-duration 500
.. note::
For more details, please run ``./vits/infer.py --help``.
Export models
-------------
Currently we only support ONNX model exporting. It will generate two files in the given ``exp-dir``:
``vits-epoch-*.onnx`` and ``vits-epoch-*.int8.onnx``.
.. code-block:: bash
$ ./vits/export-onnx.py \
--epoch 1000 \
--exp-dir vits/exp \
--tokens data/tokens.txt
You can test the exported ONNX model with:
.. code-block:: bash
$ ./vits/test_onnx.py \
--model-filename vits/exp/vits-epoch-1000.onnx \
--tokens data/tokens.txt
Download pretrained models
--------------------------
If you don't want to train from scratch, you can download the pretrained models
by visiting the following link:
- `<https://huggingface.co/Zengwei/icefall-tts-ljspeech-vits-2023-11-29>`_

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_sources/recipes/index.rst.txt
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@ -2,7 +2,7 @@ Recipes
=======
This page contains various recipes in ``icefall``.
Currently, only speech recognition recipes are provided.
Currently, we provide recipes for speech recognition, language model, and speech synthesis.
We may add recipes for other tasks as well in the future.
@ -16,3 +16,4 @@ We may add recipes for other tasks as well in the future.
Non-streaming-ASR/index
Streaming-ASR/index
RNN-LM/index
TTS/index

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@ -20,7 +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="Train an RNN language model" href="../recipes/RNN-LM/librispeech/lm-training.html" />
<link rel="prev" title="VITS" href="../recipes/TTS/ljspeech/vits.html" />
</head>
<body class="wy-body-for-nav">
@ -133,7 +135,7 @@ and code to <code class="docutils literal notranslate"><span class="pre">icefall
</div>
</div>
<footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
<a href="../recipes/RNN-LM/librispeech/lm-training.html" class="btn btn-neutral float-left" title="Train an RNN language model" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
<a href="../recipes/TTS/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="doc.html" class="btn btn-neutral float-right" title="Contributing to Documentation" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
</div>

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decoding-with-langugage-models/LODR.html
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<html class="writer-html5" lang="en">
<head>
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<title>LODR for RNN Transducer &mdash; icefall 0.1 documentation</title>
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@ -236,9 +238,9 @@ indeed <strong>further improves</strong> the WER. We can do even better if we in
<table class="docutils align-default" id="id1">
<caption><span class="caption-number">Table 2 </span><span class="caption-text">WER of LODR with different beam sizes</span><a class="headerlink" href="#id1" title="Permalink to this table"></a></caption>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 50%" />
<col style="width: 25.0%" />
<col style="width: 25.0%" />
<col style="width: 50.0%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Beam size</p></th>

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<!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 charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Decoding with language models &mdash; icefall 0.1 documentation</title>
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@ -98,8 +100,8 @@ please refer to this tutorial: <a class="reference internal" href="../recipes/RN
<p>The following decoding methods with external langugage models are available:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 33%" />
<col style="width: 67%" />
<col style="width: 33.3%" />
<col style="width: 66.7%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Decoding method</p></th>

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<head>
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<title>LM rescoring for Transducer &mdash; icefall 0.1 documentation</title>
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@ -203,9 +205,9 @@ see the following table:</p>
<table class="docutils align-default" id="id1">
<caption><span class="caption-number">Table 3 </span><span class="caption-text">WERs of LM rescoring with different beam sizes</span><a class="headerlink" href="#id1" title="Permalink to this table"></a></caption>
<colgroup>
<col style="width: 33%" />
<col style="width: 33%" />
<col style="width: 33%" />
<col style="width: 33.3%" />
<col style="width: 33.3%" />
<col style="width: 33.3%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Beam size</p></th>
@ -281,9 +283,9 @@ further improvements from LM rescoring + LODR:</p>
<table class="docutils align-default" id="id2">
<caption><span class="caption-number">Table 4 </span><span class="caption-text">WERs of LM rescoring + LODR with different beam sizes</span><a class="headerlink" href="#id2" title="Permalink to this table"></a></caption>
<colgroup>
<col style="width: 33%" />
<col style="width: 33%" />
<col style="width: 33%" />
<col style="width: 33.3%" />
<col style="width: 33.3%" />
<col style="width: 33.3%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Beam size</p></th>
@ -311,10 +313,10 @@ Here, we benchmark the WERs and decoding speed of them:</p>
<table class="docutils align-default" id="id3">
<caption><span class="caption-number">Table 5 </span><span class="caption-text">LM-rescoring-based methods vs shallow-fusion-based methods (The numbers in each field is WER on test-clean, WER on test-other and decoding time on test-clean)</span><a class="headerlink" href="#id3" title="Permalink to this table"></a></caption>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25.0%" />
<col style="width: 25.0%" />
<col style="width: 25.0%" />
<col style="width: 25.0%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Decoding method</p></th>

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@ -234,10 +236,10 @@ the LM score may dominant during decoding, leading to bad WER. A typical value o
<table class="docutils align-default" id="id2">
<caption><span class="caption-number">Table 1 </span><span class="caption-text">WERs and decoding time (on test-clean) of shallow fusion with different beam sizes</span><a class="headerlink" href="#id2" title="Permalink to this table"></a></caption>
<colgroup>
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25%" />
<col style="width: 25.0%" />
<col style="width: 25.0%" />
<col style="width: 25.0%" />
<col style="width: 25.0%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Beam size</p></th>

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<section id="vits">
<h1>VITS<a class="headerlink" href="#vits" title="Permalink to this heading"></a></h1>
<p>This tutorial shows you how to train an VITS model
with the <a class="reference external" href="https://keithito.com/LJ-Speech-Dataset/">LJSpeech</a> dataset.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The VITS paper: <a class="reference external" href="https://arxiv.org/pdf/2106.06103.pdf">Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech</a></p>
</div>
<section id="data-preparation">
<h2>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this heading"></a></h2>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>egs/ljspeech/TTS
$<span class="w"> </span>./prepare.sh
</pre></div>
</div>
<p>To run stage 1 to stage 5, use</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>./prepare.sh<span class="w"> </span>--stage<span class="w"> </span><span class="m">1</span><span class="w"> </span>--stop_stage<span class="w"> </span><span class="m">5</span>
</pre></div>
</div>
</section>
<section id="build-monotonic-alignment-search">
<h2>Build Monotonic Alignment Search<a class="headerlink" href="#build-monotonic-alignment-search" title="Permalink to this heading"></a></h2>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>vits/monotonic_align
$<span class="w"> </span>python<span class="w"> </span>setup.py<span class="w"> </span>build_ext<span class="w"> </span>--inplace
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>../../
</pre></div>
</div>
</section>
<section id="training">
<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading"></a></h2>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">export</span><span class="w"> </span><span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">&quot;0,1,2,3&quot;</span>
$<span class="w"> </span>./vits/train.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--world-size<span class="w"> </span><span class="m">4</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--num-epochs<span class="w"> </span><span class="m">1000</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>--use-fp16<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>vits/exp<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--tokens<span class="w"> </span>data/tokens.txt
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">500</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>You can adjust the hyper-parameters to control the size of the VITS model and
the training configurations. For more details, please run <code class="docutils literal notranslate"><span class="pre">./vits/train.py</span> <span class="pre">--help</span></code>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The training can take a long time (usually a couple of days).</p>
</div>
<p>Training logs, checkpoints and tensorboard logs are saved in <code class="docutils literal notranslate"><span class="pre">vits/exp</span></code>.</p>
</section>
<section id="inference">
<h2>Inference<a class="headerlink" href="#inference" title="Permalink to this heading"></a></h2>
<p>The inference part uses checkpoints saved by the training part, so you have to run the
training part first. It will save the ground-truth and generated wavs to the directory
<code class="docutils literal notranslate"><span class="pre">vits/exp/infer/epoch-*/wav</span></code>, e.g., <code class="docutils literal notranslate"><span class="pre">vits/exp/infer/epoch-1000/wav</span></code>.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">export</span><span class="w"> </span><span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">&quot;0&quot;</span>
$<span class="w"> </span>./vits/infer.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--epoch<span class="w"> </span><span class="m">1000</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--exp-dir<span class="w"> </span>vits/exp<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--tokens<span class="w"> </span>data/tokens.txt
<span class="w"> </span>--max-duration<span class="w"> </span><span class="m">500</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>For more details, please run <code class="docutils literal notranslate"><span class="pre">./vits/infer.py</span> <span class="pre">--help</span></code>.</p>
</div>
</section>
<section id="export-models">
<h2>Export models<a class="headerlink" href="#export-models" title="Permalink to this heading"></a></h2>
<p>Currently we only support ONNX model exporting. It will generate two files in the given <code class="docutils literal notranslate"><span class="pre">exp-dir</span></code>:
<code class="docutils literal notranslate"><span class="pre">vits-epoch-*.onnx</span></code> and <code class="docutils literal notranslate"><span class="pre">vits-epoch-*.int8.onnx</span></code>.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>./vits/export-onnx.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--epoch<span class="w"> </span><span class="m">1000</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--exp-dir<span class="w"> </span>vits/exp<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--tokens<span class="w"> </span>data/tokens.txt
</pre></div>
</div>
<p>You can test the exported ONNX model with:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>./vits/test_onnx.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--model-filename<span class="w"> </span>vits/exp/vits-epoch-1000.onnx<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--tokens<span class="w"> </span>data/tokens.txt
</pre></div>
</div>
</section>
<section id="download-pretrained-models">
<h2>Download pretrained models<a class="headerlink" href="#download-pretrained-models" title="Permalink to this heading"></a></h2>
<p>If you don’t want to train from scratch, you can download the pretrained models
by visiting the following link:</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference external" href="https://huggingface.co/Zengwei/icefall-tts-ljspeech-vits-2023-11-29">https://huggingface.co/Zengwei/icefall-tts-ljspeech-vits-2023-11-29</a></p></li>
</ul>
</div></blockquote>
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<html class="writer-html5" lang="en">
<head>
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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<title>Recipes &mdash; icefall 0.1 documentation</title>
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<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>
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@ -93,7 +96,7 @@
<section id="recipes">
<h1>Recipes<a class="headerlink" href="#recipes" title="Permalink to this heading"></a></h1>
<p>This page contains various recipes in <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.
Currently, only speech recognition recipes are provided.</p>
Currently, we provide recipes for speech recognition, language model, and speech synthesis.</p>
<p>We may add recipes for other tasks as well in the future.</p>
<div class="toctree-wrapper compound">
<p class="caption" role="heading"><span class="caption-text">Table of Contents</span></p>
@ -114,6 +117,10 @@ Currently, only speech recognition recipes are provided.</p>
<li class="toctree-l2"><a class="reference internal" href="RNN-LM/librispeech/lm-training.html">Train an RNN language model</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="TTS/index.html">TTS</a><ul>
<li class="toctree-l2"><a class="reference internal" href="TTS/ljspeech/vits.html">VITS</a></li>
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