import{s as R,n as Y,o as B}from"../chunks/scheduler.f7e1785c.js";import{S as O,i as D,g as i,s as o,r as J,A as K,h as s,f as n,c as l,j as G,u as N,x as v,k as I,y as Q,a,v as V,d as W,t as X,w as Z}from"../chunks/index.9f8f0838.js";import{H as ee}from"../chunks/Heading.6643a2f7.js";function te(S){let r,C,_,y,p,w,u,j=`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,c,A=`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.`,b,m,E="For these reasons, this hands-on exercise will focus on practicing the skills rather than achieving a certain metric value.",k,h,q=`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.`,H,d,z=`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.`,$,f,U=`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.`,T,x,F=`Remember, the primary aim of this exercise is to provide you with ample practice, allowing you to refine your skills and
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