audio-course/pr_201/en/chapter6/hands_on.html

<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Hands-on exercise&quot;,&quot;local&quot;:&quot;hands-on-exercise&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}">
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		<link rel="modulepreload" href="/docs/audio-course/pr_201/en/_app/immutable/chunks/EditOnGithub.5a9bb8c5.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Hands-on exercise&quot;,&quot;local&quot;:&quot;hands-on-exercise&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <h1 class="relative group"><a id="hands-on-exercise" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#hands-on-exercise"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Hands-on exercise</span></h1> <p data-svelte-h="svelte-hee2af">In this unit, we have explored text-to-speech audio task, talked about existing datasets, pretrained
models and nuances of fine-tuning SpeechT5 for a new language.</p> <p data-svelte-h="svelte-npb19z">As you’ve seen, fine-tuning models for text-to-speech task can be challenging in low-resource scenarios. At the same time,
evaluating text-to-speech models isn’t easy either.</p> <p data-svelte-h="svelte-fgqtqa">For these reasons, this hands-on exercise will focus on practicing the skills rather than achieving a certain metric value.</p> <p data-svelte-h="svelte-jfljcy">Your objective for this task is to fine-tune SpeechT5 on a dataset of your choosing. You have the freedom to select
another language from the same <code>voxpopuli</code> dataset, or you can pick any other dataset listed in this unit.</p> <p data-svelte-h="svelte-1e2ttoc">Be mindful of the training data size! For training on a free tier GPU from Google Colab, we recommend limiting the training
data to about 10-15 hours.</p> <p data-svelte-h="svelte-deblxa">Once you have completed the fine-tuning process, share your model by uploading it to the Hub. Make sure to tag your model
as a <code>text-to-speech</code> model either with appropriate kwargs, or in the Hub UI.</p> <p data-svelte-h="svelte-1taxp3r">Remember, the primary aim of this exercise is to provide you with ample practice, allowing you to refine your skills and
gain a deeper understanding of text-to-speech audio tasks.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/audio-transformers-course/blob/main/chapters/en/chapter6/hands_on.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1">&lt;</span> <span data-svelte-h="svelte-x0xyl0">&gt;</span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a>  <p></p> 
			
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