import{s as ee,o as te,n as ae}from"../chunks/scheduler.25b97de1.js";import{S as ne,i as se,g as l,s as i,r as C,A as ie,h as p,f as a,c as o,j as D,u as S,x as T,k as Z,y as oe,a as n,v as M,d as P,t as k,w as y}from"../chunks/index.d9030fc9.js";import{T as re}from"../chunks/Tip.baa67368.js";import{H as G,E as le}from"../chunks/EditOnGithub.91d95064.js";function pe(H){let s,f='XLS-R’s architecture is based on the Wav2Vec2 model, refer to <a href="wav2vec2">Wav2Vec2’s documentation page</a> for API reference.';return{c(){s=l("p"),s.innerHTML=f},l(r){s=p(r,"P",{"data-svelte-h":!0}),T(s)!=="svelte-bku3o"&&(s.innerHTML=f)},m(r,b){n(r,s,b)},p:ae,d(r){r&&a(s)}}}function me(H){let s,f,r,b,h,R,u,X,c,K=`The XLS-R model was proposed in <a href="https://arxiv.org/abs/2111.09296" rel="nofollow">XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale</a> by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman
Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.`,E,g,F="The abstract from the paper is the following:",A,d,I=`<em>This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0.
We train models with up to 2B parameters on nearly half a million hours of publicly available speech audio in 128
languages, an order of magnitude more public data than the largest known prior work. Our evaluation covers a wide range
of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation
benchmark, we improve the previous state of the art by an average of 7.4 BLEU over 21 translation directions into
English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as
VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107
language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform
English-only pretraining when translating English speech into other languages, a setting which favors monolingual
pretraining. We hope XLS-R can help to improve speech processing tasks for many more languages of the world.</em>`,V,v,J='Relevant checkpoints can be found under <a href="https://huggingface.co/models?other=xls_r" rel="nofollow">https://huggingface.co/models?other=xls_r</a>.',W,$,N='The original code can be found <a href="https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec" rel="nofollow">here</a>.',B,w,z,L,Q=`<li>XLS-R is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.</li> <li>XLS-R model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using
<a href="/docs/transformers/pr_29969/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer">Wav2Vec2CTCTokenizer</a>.</li>`,O,m,U,_,j,x,q;return h=new G({props:{title:"XLS-R",local:"xls-r",headingTag:"h1"}}),u=new G({props:{title:"Overview",local:"overview",headingTag:"h2"}}),w=new G({props:{title:"Usage tips",local:"usage-tips",headingTag:"h2"}}),m=new re({props:{$$slots:{default:[pe]},$$scope:{ctx:H}}}),_=new le({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/xls_r.md"}}),{c(){s=l("meta"),f=i(),r=l("p"),b=i(),C(h.$$.fragment),R=i(),C(u.$$.fragment),X=i(),c=l("p"),c.innerHTML=K,E=i(),g=l("p"),g.textContent=F,A=i(),d=l("p"),d.innerHTML=I,V=i(),v=l("p"),v.innerHTML=J,W=i(),$=l("p"),$.innerHTML=N,B=i(),C(w.$$.fragment),z=i(),L=l("ul"),L.innerHTML=Q,O=i(),C(m.$$.fragment),U=i(),C(_.$$.fragment),j=i(),x=l("p"),this.h()},l(e){const t=ie("svelte-u9bgzb",document.head);s=p(t,"META",{name:!0,content:!0}),t.forEach(a),f=o(e),r=p(e,"P",{}),D(r).forEach(a),b=o(e),S(h.$$.fragment,e),R=o(e),S(u.$$.fragment,e),X=o(e),c=p(e,"P",{"data-svelte-h":!0}),T(c)!=="svelte-1nyy59j"&&(c.innerHTML=K),E=o(e),g=p(e,"P",{"data-svelte-h":!0}),T(g)!=="svelte-vfdo9a"&&(g.textContent=F),A=o(e),d=p(e,"P",{"data-svelte-h":!0}),T(d)!=="svelte-ul075o"&&(d.innerHTML=I),V=o(e),v=p(e,"P",{"data-svelte-h":!0}),T(v)!=="svelte-p0mu8e"&&(v.innerHTML=J),W=o(e),$=p(e,"P",{"data-svelte-h":!0}),T($)!=="svelte-12gzw10"&&($.innerHTML=N),B=o(e),S(w.$$.fragment,e),z=o(e),L=p(e,"UL",{"data-svelte-h":!0}),T(L)!=="svelte-bxn879"&&(L.innerHTML=Q),O=o(e),S(m.$$.fragment,e),U=o(e),S(_.$$.fragment,e),j=o(e),x=p(e,"P",{}),D(x).forEach(a),this.h()},h(){Z(s,"name","hf:doc:metadata"),Z(s,"content",fe)},m(e,t){oe(document.head,s),n(e,f,t),n(e,r,t),n(e,b,t),M(h,e,t),n(e,R,t),M(u,e,t),n(e,X,t),n(e,c,t),n(e,E,t),n(e,g,t),n(e,A,t),n(e,d,t),n(e,V,t),n(e,v,t),n(e,W,t),n(e,$,t),n(e,B,t),M(w,e,t),n(e,z,t),n(e,L,t),n(e,O,t),M(m,e,t),n(e,U,t),M(_,e,t),n(e,j,t),n(e,x,t),q=!0},p(e,[t]){const Y={};t&2&&(Y.$$scope={dirty:t,ctx:e}),m.$set(Y)},i(e){q||(P(h.$$.fragment,e),P(u.$$.fragment,e),P(w.$$.fragment,e),P(m.$$.fragment,e),P(_.$$.fragment,e),q=!0)},o(e){k(h.$$.fragment,e),k(u.$$.fragment,e),k(w.$$.fragment,e),k(m.$$.fragment,e),k(_.$$.fragment,e),q=!1},d(e){e&&(a(f),a(r),a(b),a(R),a(X),a(c),a(E),a(g),a(A),a(d),a(V),a(v),a(W),a($),a(B),a(z),a(L),a(O),a(U),a(j),a(x)),a(s),y(h,e),y(u,e),y(w,e),y(m,e),y(_,e)}}}const fe='{"title":"XLS-R","local":"xls-r","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"Usage tips","local":"usage-tips","sections":[],"depth":2}],"depth":1}';function he(H){return te(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ve extends ne{constructor(s){super(),se(this,s,he,me,ee,{})}}export{ve as component};
