import{s as W,n as X,o as Z}from"../chunks/scheduler.cd324960.js";import{S as ee,i as te,e as o,s as i,c as q,h as ne,a as l,d as n,b as s,f as Q,g as A,j as $,k as V,l as ae,m as a,n as G,t as U,o as F,p as I}from"../chunks/index.d5c3adcc.js";import{C as ie,H as se,E as oe}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.92ea4747.js";function le(O){let r,C,_,b,p,w,m,P,u,R=`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.`,k,f,Y=`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.`,H,c,B="For these reasons, this hands-on exercise will focus on practicing the skills rather than achieving a certain metric value.",M,h,D=`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.`,T,d,J=`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.`,L,x,K=`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.`,E,g,N=`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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