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.8072ce1f.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
gain a deeper understanding of text-to-speech audio tasks.`,S,v,j,y,z;return p=new ie({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),m=new se({props:{title:"Hands-on exercise",local:"hands-on-exercise",headingTag:"h1"}}),v=new oe({props:{source:"https://github.com/huggingface/audio-transformers-course/blob/main/chapters/en/chapter6/hands_on.mdx"}}),{c(){r=o("meta"),C=i(),_=o("p"),b=i(),q(p.$$.fragment),w=i(),q(m.$$.fragment),P=i(),u=o("p"),u.textContent=R,k=i(),f=o("p"),f.textContent=Y,H=i(),c=o("p"),c.textContent=B,M=i(),h=o("p"),h.innerHTML=D,T=i(),d=o("p"),d.textContent=J,L=i(),x=o("p"),x.innerHTML=K,E=i(),g=o("p"),g.textContent=N,S=i(),q(v.$$.fragment),j=i(),y=o("p"),this.h()},l(e){const t=ne("svelte-u9bgzb",document.head);r=l(t,"META",{name:!0,content:!0}),t.forEach(n),C=s(e),_=l(e,"P",{}),Q(_).forEach(n),b=s(e),A(p.$$.fragment,e),w=s(e),A(m.$$.fragment,e),P=s(e),u=l(e,"P",{"data-svelte-h":!0}),$(u)!=="svelte-hee2af"&&(u.textContent=R),k=s(e),f=l(e,"P",{"data-svelte-h":!0}),$(f)!=="svelte-npb19z"&&(f.textContent=Y),H=s(e),c=l(e,"P",{"data-svelte-h":!0}),$(c)!=="svelte-fgqtqa"&&(c.textContent=B),M=s(e),h=l(e,"P",{"data-svelte-h":!0}),$(h)!=="svelte-jfljcy"&&(h.innerHTML=D),T=s(e),d=l(e,"P",{"data-svelte-h":!0}),$(d)!=="svelte-1e2ttoc"&&(d.textContent=J),L=s(e),x=l(e,"P",{"data-svelte-h":!0}),$(x)!=="svelte-deblxa"&&(x.innerHTML=K),E=s(e),g=l(e,"P",{"data-svelte-h":!0}),$(g)!=="svelte-1taxp3r"&&(g.textContent=N),S=s(e),A(v.$$.fragment,e),j=s(e),y=l(e,"P",{}),Q(y).forEach(n),this.h()},h(){V(r,"name","hf:doc:metadata"),V(r,"content",re)},m(e,t){ae(document.head,r),a(e,C,t),a(e,_,t),a(e,b,t),G(p,e,t),a(e,w,t),G(m,e,t),a(e,P,t),a(e,u,t),a(e,k,t),a(e,f,t),a(e,H,t),a(e,c,t),a(e,M,t),a(e,h,t),a(e,T,t),a(e,d,t),a(e,L,t),a(e,x,t),a(e,E,t),a(e,g,t),a(e,S,t),G(v,e,t),a(e,j,t),a(e,y,t),z=!0},p:X,i(e){z||(U(p.$$.fragment,e),U(m.$$.fragment,e),U(v.$$.fragment,e),z=!0)},o(e){F(p.$$.fragment,e),F(m.$$.fragment,e),F(v.$$.fragment,e),z=!1},d(e){e&&(n(C),n(_),n(b),n(w),n(P),n(u),n(k),n(f),n(H),n(c),n(M),n(h),n(T),n(d),n(L),n(x),n(E),n(g),n(S),n(j),n(y)),n(r),I(p,e),I(m,e),I(v,e)}}}const re='{"title":"Hands-on exercise","local":"hands-on-exercise","sections":[],"depth":1}';function pe(O){return Z(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ce extends ee{constructor(r){super(),te(this,r,pe,le,W,{})}}export{ce as component};
