import{s as Q,n as V,o as W}from"../chunks/scheduler.f7e1785c.js";import{S as X,i as Z,g as o,s as i,r as O,A as ee,h as l,f as n,c as s,j as R,u as Y,x as g,k as B,y as te,a,v as D,d as J,t as K,w as N}from"../chunks/index.9f8f0838.js";import{H as ne,E as ae}from"../chunks/EditOnGithub.5a9bb8c5.js";function ie(j){let r,b,_,y,u,$,p,A=`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.`,w,m,q=`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,c,z="For these reasons, this hands-on exercise will focus on practicing the skills rather than achieving a certain metric value.",k,h,G=`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,f,U=`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.`,T,d,F=`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.`,M,x,I=`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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