import{S as Fo,i as Ho,s as So,e as n,k as c,w as g,t as a,M as qo,c as o,d as t,m as h,a as r,x as b,h as l,b as d,G as s,g as i,y as w,q as J,o as _,B as U,v as Do,L as Po}from"../../chunks/vendor-hf-doc-builder.js";import{T as Sa}from"../../chunks/Tip-hf-doc-builder.js";import{Y as Lo}from"../../chunks/Youtube-hf-doc-builder.js";import{I as pt}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as V}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as Oo}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";import{F as Qo,M as zo}from"../../chunks/Markdown-hf-doc-builder.js";function Ko(D){let m,y,u,j,$,v,A,Z,k,E,G,I,Q,B,z,P,X,C,L,q,Y,x,F,N,M,R,H,ne,S,O,K;return{c(){m=a(`The task illustrated in this tutorial is supported by the following model architectures:

`),y=n("p"),u=n("a"),j=a("Data2VecAudio"),$=a(", "),v=n("a"),A=a("Hubert"),Z=a(", "),k=n("a"),E=a("M-CTC-T"),G=a(", "),I=n("a"),Q=a("SEW"),B=a(", "),z=n("a"),P=a("SEW-D"),X=a(", "),C=n("a"),L=a("UniSpeech"),q=a(", "),Y=n("a"),x=a("UniSpeechSat"),F=a(", "),N=n("a"),M=a("Wav2Vec2"),R=a(", "),H=n("a"),ne=a("Wav2Vec2-Conformer"),S=a(", "),O=n("a"),K=a("WavLM"),this.h()},l(ee){m=l(ee,`The task illustrated in this tutorial is supported by the following model architectures:

`),y=o(ee,"P",{});var T=r(y);u=o(T,"A",{href:!0});var ae=r(u);j=l(ae,"Data2VecAudio"),ae.forEach(t),$=l(T,", "),v=o(T,"A",{href:!0});var oe=r(v);A=l(oe,"Hubert"),oe.forEach(t),Z=l(T,", "),k=o(T,"A",{href:!0});var he=r(k);E=l(he,"M-CTC-T"),he.forEach(t),G=l(T,", "),I=o(T,"A",{href:!0});var $e=r(I);Q=l($e,"SEW"),$e.forEach(t),B=l(T,", "),z=o(T,"A",{href:!0});var me=r(z);P=l(me,"SEW-D"),me.forEach(t),X=l(T,", "),C=o(T,"A",{href:!0});var re=r(C);L=l(re,"UniSpeech"),re.forEach(t),q=l(T,", "),Y=o(T,"A",{href:!0});var ve=r(Y);x=l(ve,"UniSpeechSat"),ve.forEach(t),F=l(T,", "),N=o(T,"A",{href:!0});var ue=r(N);M=l(ue,"Wav2Vec2"),ue.forEach(t),R=l(T,", "),H=o(T,"A",{href:!0});var pe=r(H);ne=l(pe,"Wav2Vec2-Conformer"),pe.forEach(t),S=l(T,", "),O=o(T,"A",{href:!0});var Te=r(O);K=l(Te,"WavLM"),Te.forEach(t),T.forEach(t),this.h()},h(){d(u,"href","../model_doc/data2vec-audio"),d(v,"href","../model_doc/hubert"),d(k,"href","../model_doc/mctct"),d(I,"href","../model_doc/sew"),d(z,"href","../model_doc/sew-d"),d(C,"href","../model_doc/unispeech"),d(Y,"href","../model_doc/unispeech-sat"),d(N,"href","../model_doc/wav2vec2"),d(H,"href","../model_doc/wav2vec2-conformer"),d(O,"href","../model_doc/wavlm")},m(ee,T){i(ee,m,T),i(ee,y,T),s(y,u),s(u,j),s(y,$),s(y,v),s(v,A),s(y,Z),s(y,k),s(k,E),s(y,G),s(y,I),s(I,Q),s(y,B),s(y,z),s(z,P),s(y,X),s(y,C),s(C,L),s(y,q),s(y,Y),s(Y,x),s(y,F),s(y,N),s(N,M),s(y,R),s(y,H),s(H,ne),s(y,S),s(y,O),s(O,K)},d(ee){ee&&t(m),ee&&t(y)}}}function er(D){let m,y,u,j,$,v,A,Z;return{c(){m=n("p"),y=a("If you aren\u2019t familiar with finetuning a model with the "),u=n("a"),j=a("Trainer"),$=a(", take a look at the basic tutorial "),v=n("a"),A=a("here"),Z=a("!"),this.h()},l(k){m=o(k,"P",{});var E=r(m);y=l(E,"If you aren\u2019t familiar with finetuning a model with the "),u=o(E,"A",{href:!0});var G=r(u);j=l(G,"Trainer"),G.forEach(t),$=l(E,", take a look at the basic tutorial "),v=o(E,"A",{href:!0});var I=r(v);A=l(I,"here"),I.forEach(t),Z=l(E,"!"),E.forEach(t),this.h()},h(){d(u,"href","/docs/transformers/v4.32.0/en/main_classes/trainer#transformers.Trainer"),d(v,"href","../training#train-with-pytorch-trainer")},m(k,E){i(k,m,E),s(m,y),s(m,u),s(u,j),s(m,$),s(m,v),s(v,A),s(m,Z)},d(k){k&&t(m)}}}function sr(D){let m,y,u,j,$,v,A,Z,k,E,G,I,Q,B,z,P,X,C,L,q,Y,x,F,N,M,R,H,ne,S,O,K,ee,T,ae,oe,he,$e,me,re,ve,ue,pe,Te,ie,Qe,de,fe,ye,ge,be,Ae,Ee,ze,we,Fe,le,He;return m=new Sa({props:{$$slots:{default:[er]},$$scope:{ctx:D}}}),I=new V({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvckNUQyUyQyUyMFRyYWluaW5nQXJndW1lbnRzJTJDJTIwVHJhaW5lciUwQSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQ1RDLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJmYWNlYm9vayUyRndhdjJ2ZWMyLWJhc2UlMjIlMkMlMEElMjAlMjAlMjAlMjBjdGNfbG9zc19yZWR1Y3Rpb24lM0QlMjJtZWFuJTIyJTJDJTBBJTIwJTIwJTIwJTIwcGFkX3Rva2VuX2lkJTNEcHJvY2Vzc29yLnRva2VuaXplci5wYWRfdG9rZW5faWQlMkMlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCTC, TrainingArguments, Trainer

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;facebook/wav2vec2-base&quot;</span>,
<span class="hljs-meta">... </span>    ctc_loss_reduction=<span class="hljs-string">&quot;mean&quot;</span>,
<span class="hljs-meta">... </span>    pad_token_id=processor.tokenizer.pad_token_id,
<span class="hljs-meta">... </span>)`}}),ye=new V({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span>    output_dir=<span class="hljs-string">&quot;my_awesome_asr_mind_model&quot;</span>,
<span class="hljs-meta">... </span>    per_device_train_batch_size=<span class="hljs-number">8</span>,
<span class="hljs-meta">... </span>    gradient_accumulation_steps=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>    learning_rate=<span class="hljs-number">1e-5</span>,
<span class="hljs-meta">... </span>    warmup_steps=<span class="hljs-number">500</span>,
<span class="hljs-meta">... </span>    max_steps=<span class="hljs-number">2000</span>,
<span class="hljs-meta">... </span>    gradient_checkpointing=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    fp16=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    group_by_length=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    evaluation_strategy=<span class="hljs-string">&quot;steps&quot;</span>,
<span class="hljs-meta">... </span>    per_device_eval_batch_size=<span class="hljs-number">8</span>,
<span class="hljs-meta">... </span>    save_steps=<span class="hljs-number">1000</span>,
<span class="hljs-meta">... </span>    eval_steps=<span class="hljs-number">1000</span>,
<span class="hljs-meta">... </span>    logging_steps=<span class="hljs-number">25</span>,
<span class="hljs-meta">... </span>    load_best_model_at_end=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    metric_for_best_model=<span class="hljs-string">&quot;wer&quot;</span>,
<span class="hljs-meta">... </span>    greater_is_better=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    push_to_hub=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>trainer = Trainer(
<span class="hljs-meta">... </span>    model=model,
<span class="hljs-meta">... </span>    args=training_args,
<span class="hljs-meta">... </span>    train_dataset=encoded_minds[<span class="hljs-string">&quot;train&quot;</span>],
<span class="hljs-meta">... </span>    eval_dataset=encoded_minds[<span class="hljs-string">&quot;test&quot;</span>],
<span class="hljs-meta">... </span>    tokenizer=processor,
<span class="hljs-meta">... </span>    data_collator=data_collator,
<span class="hljs-meta">... </span>    compute_metrics=compute_metrics,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.train()`}}),le=new V({props:{code:"dHJhaW5lci5wdXNoX3RvX2h1Yigp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.push_to_hub()'}}),{c(){g(m.$$.fragment),y=c(),u=n("p"),j=a("You\u2019re ready to start training your model now! Load Wav2Vec2 with "),$=n("a"),v=a("AutoModelForCTC"),A=a(". Specify the reduction to apply with the "),Z=n("code"),k=a("ctc_loss_reduction"),E=a(" parameter. It is often better to use the average instead of the default summation:"),G=c(),g(I.$$.fragment),Q=c(),B=n("p"),z=a("At this point, only three steps remain:"),P=c(),X=n("ol"),C=n("li"),L=a("Define your training hyperparameters in "),q=n("a"),Y=a("TrainingArguments"),x=a(". The only required parameter is "),F=n("code"),N=a("output_dir"),M=a(" which specifies where to save your model. You\u2019ll push this model to the Hub by setting "),R=n("code"),H=a("push_to_hub=True"),ne=a(" (you need to be signed in to Hugging Face to upload your model). 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At the end of each epoch, the "),S=o(se,"A",{href:!0});var Ss=r(S);O=l(Ss,"Trainer"),Ss.forEach(t),K=l(se," will evaluate the WER and save the training checkpoint."),se.forEach(t),ee=h(ke),T=o(ke,"LI",{});var Ie=r(T);ae=l(Ie,"Pass the training arguments to "),oe=o(Ie,"A",{href:!0});var fs=r(oe);he=l(fs,"Trainer"),fs.forEach(t),$e=l(Ie," along with the model, dataset, tokenizer, data collator, and "),me=o(Ie,"CODE",{});var Ce=r(me);re=l(Ce,"compute_metrics"),Ce.forEach(t),ve=l(Ie," function."),Ie.forEach(t),ue=h(ke),pe=o(ke,"LI",{});var Ge=r(pe);Te=l(Ge,"Call "),ie=o(Ge,"A",{href:!0});var Be=r(ie);Qe=l(Be,"train()"),Be.forEach(t),de=l(Ge," to finetune your model."),Ge.forEach(t),ke.forEach(t),fe=h(f),b(ye.$$.fragment,f),ge=h(f),be=o(f,"P",{});var qe=r(be);Ae=l(qe,"Once training is completed, share your model to the Hub with the "),Ee=o(qe,"A",{href:!0});var ys=r(Ee);ze=l(ys,"push_to_hub()"),ys.forEach(t),we=l(qe," method so everyone can use your model:"),qe.forEach(t),Fe=h(f),b(le.$$.fragment,f),this.h()},h(){d($,"href","/docs/transformers/v4.32.0/en/model_doc/auto#transformers.AutoModelForCTC"),d(q,"href","/docs/transformers/v4.32.0/en/main_classes/trainer#transformers.TrainingArguments"),d(S,"href","/docs/transformers/v4.32.0/en/main_classes/trainer#transformers.Trainer"),d(oe,"href","/docs/transformers/v4.32.0/en/main_classes/trainer#transformers.Trainer"),d(ie,"href","/docs/transformers/v4.32.0/en/main_classes/trainer#transformers.Trainer.train"),d(Ee,"href","/docs/transformers/v4.32.0/en/main_classes/trainer#transformers.Trainer.push_to_hub")},m(f,W){w(m,f,W),i(f,y,W),i(f,u,W),s(u,j),s(u,$),s($,v),s(u,A),s(u,Z),s(Z,k),s(u,E),i(f,G,W),w(I,f,W),i(f,Q,W),i(f,B,W),s(B,z),i(f,P,W),i(f,X,W),s(X,C),s(C,L),s(C,q),s(q,Y),s(C,x),s(C,F),s(F,N),s(C,M),s(C,R),s(R,H),s(C,ne),s(C,S),s(S,O),s(C,K),s(X,ee),s(X,T),s(T,ae),s(T,oe),s(oe,he),s(T,$e),s(T,me),s(me,re),s(T,ve),s(X,ue),s(X,pe),s(pe,Te),s(pe,ie),s(ie,Qe),s(pe,de),i(f,fe,W),w(ye,f,W),i(f,ge,W),i(f,be,W),s(be,Ae),s(be,Ee),s(Ee,ze),s(be,we),i(f,Fe,W),w(le,f,W),He=!0},p(f,W){const Me={};W&2&&(Me.$$scope={dirty:W,ctx:f}),m.$set(Me)},i(f){He||(J(m.$$.fragment,f),J(I.$$.fragment,f),J(ye.$$.fragment,f),J(le.$$.fragment,f),He=!0)},o(f){_(m.$$.fragment,f),_(I.$$.fragment,f),_(ye.$$.fragment,f),_(le.$$.fragment,f),He=!1},d(f){U(m,f),f&&t(y),f&&t(u),f&&t(G),U(I,f),f&&t(Q),f&&t(B),f&&t(P),f&&t(X),f&&t(fe),U(ye,f),f&&t(ge),f&&t(be),f&&t(Fe),U(le,f)}}}function tr(D){let m,y;return m=new zo({props:{$$slots:{default:[sr]},$$scope:{ctx:D}}}),{c(){g(m.$$.fragment)},l(u){b(m.$$.fragment,u)},m(u,j){w(m,u,j),y=!0},p(u,j){const $={};j&2&&($.$$scope={dirty:j,ctx:u}),m.$set($)},i(u){y||(J(m.$$.fragment,u),y=!0)},o(u){_(m.$$.fragment,u),y=!1},d(u){U(m,u)}}}function ar(D){let m,y,u,j,$,v,A,Z;return{c(){m=n("p"),y=a("For a more in-depth example of how to finetune a model for automatic speech recognition, take a look at this blog "),u=n("a"),j=a("post"),$=a(" for English ASR and this "),v=n("a"),A=a("post"),Z=a(" for multilingual ASR."),this.h()},l(k){m=o(k,"P",{});var E=r(m);y=l(E,"For a more in-depth example of how to finetune a model for automatic speech recognition, take a look at this blog "),u=o(E,"A",{href:!0,rel:!0});var G=r(u);j=l(G,"post"),G.forEach(t),$=l(E," for English ASR and this "),v=o(E,"A",{href:!0,rel:!0});var I=r(v);A=l(I,"post"),I.forEach(t),Z=l(E," for multilingual ASR."),E.forEach(t),this.h()},h(){d(u,"href","https://huggingface.co/blog/fine-tune-wav2vec2-english"),d(u,"rel","nofollow"),d(v,"href","https://huggingface.co/blog/fine-tune-xlsr-wav2vec2"),d(v,"rel","nofollow")},m(k,E){i(k,m,E),s(m,y),s(m,u),s(u,j),s(m,$),s(m,v),s(v,A),s(m,Z)},d(k){k&&t(m)}}}function lr(D){let m,y;return{c(){m=n("p"),y=a("The transcription is decent, but it could be better! Try finetuning your model on more examples to get even better results!")},l(u){m=o(u,"P",{});var j=r(m);y=l(j,"The transcription is decent, but it could be better! Try finetuning your model on more examples to get even better results!"),j.forEach(t)},m(u,j){i(u,m,j),s(m,y)},d(u){u&&t(m)}}}function nr(D){let m,y,u,j,$,v,A,Z,k,E,G,I,Q,B,z,P,X,C,L,q,Y,x,F,N;return A=new V({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Qcm9jZXNzb3IlMEElMEFwcm9jZXNzb3IlMjAlM0QlMjBBdXRvUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJzdGV2aGxpdSUyRm15X2F3ZXNvbWVfYXNyX21pbmRfbW9kZWwlMjIpJTBBaW5wdXRzJTIwJTNEJTIwcHJvY2Vzc29yKGRhdGFzZXQlNUIwJTVEJTVCJTIyYXVkaW8lMjIlNUQlNUIlMjJhcnJheSUyMiU1RCUyQyUyMHNhbXBsaW5nX3JhdGUlM0RzYW1wbGluZ19yYXRlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;stevhliu/my_awesome_asr_mind_model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], sampling_rate=sampling_rate, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)`}}),I=new V({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvckNUQyUwQSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQ1RDLmZyb21fcHJldHJhaW5lZCglMjJzdGV2aGxpdSUyRm15X2F3ZXNvbWVfYXNyX21pbmRfbW9kZWwlMjIpJTBBd2l0aCUyMHRvcmNoLm5vX2dyYWQoKSUzQSUwQSUyMCUyMCUyMCUyMGxvZ2l0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKS5sb2dpdHM=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCTC

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(<span class="hljs-string">&quot;stevhliu/my_awesome_asr_mind_model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits`}}),F=new V({props:{code:"aW1wb3J0JTIwdG9yY2glMEElMEFwcmVkaWN0ZWRfaWRzJTIwJTNEJTIwdG9yY2guYXJnbWF4KGxvZ2l0cyUyQyUyMGRpbSUzRC0xKSUwQXRyYW5zY3JpcHRpb24lMjAlM0QlMjBwcm9jZXNzb3IuYmF0Y2hfZGVjb2RlKHByZWRpY3RlZF9pZHMpJTBBdHJhbnNjcmlwdGlvbg==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_ids = torch.argmax(logits, dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>transcription = processor.batch_decode(predicted_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>transcription
[<span class="hljs-string">&#x27;I WOUL LIKE O SET UP JOINT ACOUNT WTH Y PARTNER&#x27;</span>]`}}),{c(){m=n("p"),y=a("Load a processor to preprocess the audio file and transcription and return the "),u=n("code"),j=a("input"),$=a(" as PyTorch tensors:"),v=c(),g(A.$$.fragment),Z=c(),k=n("p"),E=a("Pass your inputs to the model and return the logits:"),G=c(),g(I.$$.fragment),Q=c(),B=n("p"),z=a("Get the predicted "),P=n("code"),X=a("input_ids"),C=a(" with the highest probability, and use the processor to decode the predicted "),L=n("code"),q=a("input_ids"),Y=a(" back into text:"),x=c(),g(F.$$.fragment)},l(M){m=o(M,"P",{});var R=r(m);y=l(R,"Load a processor to preprocess the audio file and transcription and return the "),u=o(R,"CODE",{});var H=r(u);j=l(H,"input"),H.forEach(t),$=l(R," as PyTorch tensors:"),R.forEach(t),v=h(M),b(A.$$.fragment,M),Z=h(M),k=o(M,"P",{});var ne=r(k);E=l(ne,"Pass your inputs to the model and return the logits:"),ne.forEach(t),G=h(M),b(I.$$.fragment,M),Q=h(M),B=o(M,"P",{});var S=r(B);z=l(S,"Get the predicted "),P=o(S,"CODE",{});var O=r(P);X=l(O,"input_ids"),O.forEach(t),C=l(S," with the highest probability, and use the processor to decode the predicted "),L=o(S,"CODE",{});var K=r(L);q=l(K,"input_ids"),K.forEach(t),Y=l(S," back into text:"),S.forEach(t),x=h(M),b(F.$$.fragment,M)},m(M,R){i(M,m,R),s(m,y),s(m,u),s(u,j),s(m,$),i(M,v,R),w(A,M,R),i(M,Z,R),i(M,k,R),s(k,E),i(M,G,R),w(I,M,R),i(M,Q,R),i(M,B,R),s(B,z),s(B,P),s(P,X),s(B,C),s(B,L),s(L,q),s(B,Y),i(M,x,R),w(F,M,R),N=!0},p:Po,i(M){N||(J(A.$$.fragment,M),J(I.$$.fragment,M),J(F.$$.fragment,M),N=!0)},o(M){_(A.$$.fragment,M),_(I.$$.fragment,M),_(F.$$.fragment,M),N=!1},d(M){M&&t(m),M&&t(v),U(A,M),M&&t(Z),M&&t(k),M&&t(G),U(I,M),M&&t(Q),M&&t(B),M&&t(x),U(F,M)}}}function or(D){let m,y;return m=new zo({props:{$$slots:{default:[nr]},$$scope:{ctx:D}}}),{c(){g(m.$$.fragment)},l(u){b(m.$$.fragment,u)},m(u,j){w(m,u,j),y=!0},p(u,j){const $={};j&2&&($.$$scope={dirty:j,ctx:u}),m.$set($)},i(u){y||(J(m.$$.fragment,u),y=!0)},o(u){_(m.$$.fragment,u),y=!1},d(u){U(m,u)}}}function rr(D){let m,y,u,j,$,v,A,Z,k,E,G,I,Q,B,z,P,X,C,L,q,Y,x,F,N,M,R,H,ne,S,O,K,ee,T,ae,oe,he,$e,me,re,ve,ue,pe,Te,ie,Qe,de,fe,ye,ge,be,Ae,Ee,ze,we,Fe,le,He,f,W,Me,ds,je,ke,se,Fs,Hs,Se,Ss,Ie,fs,Ce,Ge,Be,qe,ys,Ms,Qt,te,qa,it,Da,Pa,ct,La,Oa,ht,Ka,el,mt,sl,tl,js,al,ll,zt,gs,Ft,qs,nl,Ht,bs,St,Ds,ol,qt,De,Pe,ut,rl,pl,dt,il,cl,hl,Ps,ft,ml,ul,Dt,Ve,Le,yt,ws,dl,Mt,fl,Pt,Ls,yl,Lt,Js,Ot,Oe,Ml,_s,jl,gl,Kt,Us,ea,Ke,bl,jt,wl,Jl,sa,vs,ta,Os,_l,aa,es,Ts,Ul,gt,vl,Tl,$l,Xe,El,bt,kl,Il,wt,Cl,Rl,la,$s,na,ce,Wl,Es,Zl,Al,Jt,Gl,Bl,_t,Vl,Xl,ks,Yl,Nl,oa,Is,ra,Je,xl,Ks,Ql,zl,Ut,Fl,Hl,vt,Sl,ql,pa,Re,Dl,Tt,Pl,Ll,$t,Ol,Kl,ia,Cs,ca,ss,en,Et,sn,tn,ha,Rs,ma,Ye,ts,kt,Ws,an,It,ln,ua,_e,nn,Zs,on,rn,As,pn,cn,Gs,hn,mn,da,Bs,fa,as,un,Vs,dn,fn,ya,Xs,Ma,ls,yn,Ct,Mn,jn,ja,Ne,ns,Rt,Ys,gn,Wt,bn,ga,os,ba,rs,wa,xe,ps,Zt,Ns,wn,At,Jn,Ja,et,_n,_a,st,Un,Ua,xs,va,We,vn,tt,Tn,$n,Gt,En,kn,Ta,Qs,$a,is,Ea,cs,In,Bt,Cn,Rn,ka,hs,Ia;return v=new pt({}),G=new Oo({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/asr.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/pytorch/asr.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/tensorflow/asr.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/asr.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/pytorch/asr.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/tensorflow/asr.ipynb"}]}}),Q=new Lo({props:{id:"TksaY_FDgnk"}}),ae=new Sa({props:{$$slots:{default:[Ko]},$$scope:{ctx:D}}}),re=new V({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycyUyMGRhdGFzZXRzJTIwZXZhbHVhdGUlMjBqaXdlcg==",highlighted:"pip install transformers datasets evaluate jiwer"}}),ie=new V({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

<span class="hljs-meta">&gt;&gt;&gt; </span>notebook_login()`}}),ge=new pt({}),Me=new V({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTJDJTIwQXVkaW8lMEElMEFtaW5kcyUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJQb2x5QUklMkZtaW5kczE0JTIyJTJDJTIwbmFtZSUzRCUyMmVuLVVTJTIyJTJDJTIwc3BsaXQlM0QlMjJ0cmFpbiU1QiUzQTEwMCU1RCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset, Audio

<span class="hljs-meta">&gt;&gt;&gt; </span>minds = load_dataset(<span class="hljs-string">&quot;PolyAI/minds14&quot;</span>, name=<span class="hljs-string">&quot;en-US&quot;</span>, split=<span class="hljs-string">&quot;train[:100]&quot;</span>)`}}),Ce=new V({props:{code:"bWluZHMlMjAlM0QlMjBtaW5kcy50cmFpbl90ZXN0X3NwbGl0KHRlc3Rfc2l6ZSUzRDAuMik=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>minds = minds.train_test_split(test_size=<span class="hljs-number">0.2</span>)'}}),Ms=new V({props:{code:"bWluZHM=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>minds
DatasetDict({
    train: Dataset({
        features: [<span class="hljs-string">&#x27;path&#x27;</span>, <span class="hljs-string">&#x27;audio&#x27;</span>, <span class="hljs-string">&#x27;transcription&#x27;</span>, <span class="hljs-string">&#x27;english_transcription&#x27;</span>, <span class="hljs-string">&#x27;intent_class&#x27;</span>, <span class="hljs-string">&#x27;lang_id&#x27;</span>],
        num_rows: <span class="hljs-number">16</span>
    })
    test: Dataset({
        features: [<span class="hljs-string">&#x27;path&#x27;</span>, <span class="hljs-string">&#x27;audio&#x27;</span>, <span class="hljs-string">&#x27;transcription&#x27;</span>, <span class="hljs-string">&#x27;english_transcription&#x27;</span>, <span class="hljs-string">&#x27;intent_class&#x27;</span>, <span class="hljs-string">&#x27;lang_id&#x27;</span>],
        num_rows: <span class="hljs-number">4</span>
    })
})`}}),gs=new V({props:{code:"bWluZHMlMjAlM0QlMjBtaW5kcy5yZW1vdmVfY29sdW1ucyglNUIlMjJlbmdsaXNoX3RyYW5zY3JpcHRpb24lMjIlMkMlMjAlMjJpbnRlbnRfY2xhc3MlMjIlMkMlMjAlMjJsYW5nX2lkJTIyJTVEKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>minds = minds.remove_columns([<span class="hljs-string">&quot;english_transcription&quot;</span>, <span class="hljs-string">&quot;intent_class&quot;</span>, <span class="hljs-string">&quot;lang_id&quot;</span>])'}}),bs=new V({props:{code:"bWluZHMlNUIlMjJ0cmFpbiUyMiU1RCU1QjAlNUQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>minds[<span class="hljs-string">&quot;train&quot;</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">&#x27;audio&#x27;</span>: {<span class="hljs-string">&#x27;array&#x27;</span>: array([-<span class="hljs-number">0.00024414</span>,  <span class="hljs-number">0.</span>        ,  <span class="hljs-number">0.</span>        , ...,  <span class="hljs-number">0.00024414</span>,
          <span class="hljs-number">0.00024414</span>,  <span class="hljs-number">0.00024414</span>], dtype=float32),
  <span class="hljs-string">&#x27;path&#x27;</span>: <span class="hljs-string">&#x27;/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav&#x27;</span>,
  <span class="hljs-string">&#x27;sampling_rate&#x27;</span>: <span class="hljs-number">8000</span>},
 <span class="hljs-string">&#x27;path&#x27;</span>: <span class="hljs-string">&#x27;/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav&#x27;</span>,
 <span class="hljs-string">&#x27;transcription&#x27;</span>: <span class="hljs-string">&quot;hi I&#x27;m trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing&quot;</span>}`}}),ws=new pt({}),Js=new V({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Qcm9jZXNzb3IlMEElMEFwcm9jZXNzb3IlMjAlM0QlMjBBdXRvUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJmYWNlYm9vayUyRndhdjJ2ZWMyLWJhc2UlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base&quot;</span>)`}}),Us=new V({props:{code:"bWluZHMlMjAlM0QlMjBtaW5kcy5jYXN0X2NvbHVtbiglMjJhdWRpbyUyMiUyQyUyMEF1ZGlvKHNhbXBsaW5nX3JhdGUlM0QxNl8wMDApKSUwQW1pbmRzJTVCJTIydHJhaW4lMjIlNUQlNUIwJTVE",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>minds = minds.cast_column(<span class="hljs-string">&quot;audio&quot;</span>, Audio(sampling_rate=<span class="hljs-number">16_000</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>minds[<span class="hljs-string">&quot;train&quot;</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">&#x27;audio&#x27;</span>: {<span class="hljs-string">&#x27;array&#x27;</span>: array([-<span class="hljs-number">2.38064706e-04</span>, -<span class="hljs-number">1.58618059e-04</span>, -<span class="hljs-number">5.43987835e-06</span>, ...,
          <span class="hljs-number">2.78103951e-04</span>,  <span class="hljs-number">2.38446111e-04</span>,  <span class="hljs-number">1.18740834e-04</span>], dtype=float32),
  <span class="hljs-string">&#x27;path&#x27;</span>: <span class="hljs-string">&#x27;/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav&#x27;</span>,
  <span class="hljs-string">&#x27;sampling_rate&#x27;</span>: <span class="hljs-number">16000</span>},
 <span class="hljs-string">&#x27;path&#x27;</span>: <span class="hljs-string">&#x27;/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav&#x27;</span>,
 <span class="hljs-string">&#x27;transcription&#x27;</span>: <span class="hljs-string">&quot;hi I&#x27;m trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing&quot;</span>}`}}),vs=new V({props:{code:"ZGVmJTIwdXBwZXJjYXNlKGV4YW1wbGUpJTNBJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwJTdCJTIydHJhbnNjcmlwdGlvbiUyMiUzQSUyMGV4YW1wbGUlNUIlMjJ0cmFuc2NyaXB0aW9uJTIyJTVELnVwcGVyKCklN0QlMEElMEElMEFtaW5kcyUyMCUzRCUyMG1pbmRzLm1hcCh1cHBlcmNhc2Up",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">uppercase</span>(<span class="hljs-params">example</span>):
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;transcription&quot;</span>: example[<span class="hljs-string">&quot;transcription&quot;</span>].upper()}


<span class="hljs-meta">&gt;&gt;&gt; </span>minds = minds.<span class="hljs-built_in">map</span>(uppercase)`}}),$s=new V({props:{code:"ZGVmJTIwcHJlcGFyZV9kYXRhc2V0KGJhdGNoKSUzQSUwQSUyMCUyMCUyMCUyMGF1ZGlvJTIwJTNEJTIwYmF0Y2glNUIlMjJhdWRpbyUyMiU1RCUwQSUyMCUyMCUyMCUyMGJhdGNoJTIwJTNEJTIwcHJvY2Vzc29yKGF1ZGlvJTVCJTIyYXJyYXklMjIlNUQlMkMlMjBzYW1wbGluZ19yYXRlJTNEYXVkaW8lNUIlMjJzYW1wbGluZ19yYXRlJTIyJTVEJTJDJTIwdGV4dCUzRGJhdGNoJTVCJTIydHJhbnNjcmlwdGlvbiUyMiU1RCklMEElMjAlMjAlMjAlMjBiYXRjaCU1QiUyMmlucHV0X2xlbmd0aCUyMiU1RCUyMCUzRCUyMGxlbihiYXRjaCU1QiUyMmlucHV0X3ZhbHVlcyUyMiU1RCU1QjAlNUQpJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwYmF0Y2g=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">prepare_dataset</span>(<span class="hljs-params">batch</span>):
<span class="hljs-meta">... </span>    audio = batch[<span class="hljs-string">&quot;audio&quot;</span>]
<span class="hljs-meta">... </span>    batch = processor(audio[<span class="hljs-string">&quot;array&quot;</span>], sampling_rate=audio[<span class="hljs-string">&quot;sampling_rate&quot;</span>], text=batch[<span class="hljs-string">&quot;transcription&quot;</span>])
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;input_length&quot;</span>] = <span class="hljs-built_in">len</span>(batch[<span class="hljs-string">&quot;input_values&quot;</span>][<span class="hljs-number">0</span>])
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> batch`}}),Is=new V({props:{code:"ZW5jb2RlZF9taW5kcyUyMCUzRCUyMG1pbmRzLm1hcChwcmVwYXJlX2RhdGFzZXQlMkMlMjByZW1vdmVfY29sdW1ucyUzRG1pbmRzLmNvbHVtbl9uYW1lcyU1QiUyMnRyYWluJTIyJTVEJTJDJTIwbnVtX3Byb2MlM0Q0KQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_minds = minds.<span class="hljs-built_in">map</span>(prepare_dataset, remove_columns=minds.column_names[<span class="hljs-string">&quot;train&quot;</span>], num_proc=<span class="hljs-number">4</span>)'}}),Cs=new V({props:{code:"aW1wb3J0JTIwdG9yY2glMEElMEFmcm9tJTIwZGF0YWNsYXNzZXMlMjBpbXBvcnQlMjBkYXRhY2xhc3MlMkMlMjBmaWVsZCUwQWZyb20lMjB0eXBpbmclMjBpbXBvcnQlMjBBbnklMkMlMjBEaWN0JTJDJTIwTGlzdCUyQyUyME9wdGlvbmFsJTJDJTIwVW5pb24lMEElMEElMEElNDBkYXRhY2xhc3MlMEFjbGFzcyUyMERhdGFDb2xsYXRvckNUQ1dpdGhQYWRkaW5nJTNBJTBBJTIwJTIwJTIwJTIwcHJvY2Vzc29yJTNBJTIwQXV0b1Byb2Nlc3NvciUwQSUyMCUyMCUyMCUyMHBhZGRpbmclM0ElMjBVbmlvbiU1QmJvb2wlMkMlMjBzdHIlNUQlMjAlM0QlMjAlMjJsb25nZXN0JTIyJTBBJTBBJTIwJTIwJTIwJTIwZGVmJTIwX19jYWxsX18oc2VsZiUyQyUyMGZlYXR1cmVzJTNBJTIwTGlzdCU1QkRpY3QlNUJzdHIlMkMlMjBVbmlvbiU1Qkxpc3QlNUJpbnQlNUQlMkMlMjB0b3JjaC5UZW5zb3IlNUQlNUQlNUQpJTIwLSUzRSUyMERpY3QlNUJzdHIlMkMlMjB0b3JjaC5UZW5zb3IlNUQlM0ElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjMlMjBzcGxpdCUyMGlucHV0cyUyMGFuZCUyMGxhYmVscyUyMHNpbmNlJTIwdGhleSUyMGhhdmUlMjB0byUyMGJlJTIwb2YlMjBkaWZmZXJlbnQlMjBsZW5ndGhzJTIwYW5kJTIwbmVlZCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMyUyMGRpZmZlcmVudCUyMHBhZGRpbmclMjBtZXRob2RzJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwaW5wdXRfZmVhdHVyZXMlMjAlM0QlMjAlNUIlN0IlMjJpbnB1dF92YWx1ZXMlMjIlM0ElMjBmZWF0dXJlJTVCJTIyaW5wdXRfdmFsdWVzJTIyJTVEJTVCMCU1RCU3RCUyMGZvciUyMGZlYXR1cmUlMjBpbiUyMGZlYXR1cmVzJTVEJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwbGFiZWxfZmVhdHVyZXMlMjAlM0QlMjAlNUIlN0IlMjJpbnB1dF9pZHMlMjIlM0ElMjBmZWF0dXJlJTVCJTIybGFiZWxzJTIyJTVEJTdEJTIwZm9yJTIwZmVhdHVyZSUyMGluJTIwZmVhdHVyZXMlNUQlMEElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBiYXRjaCUyMCUzRCUyMHNlbGYucHJvY2Vzc29yLnBhZChpbnB1dF9mZWF0dXJlcyUyQyUyMHBhZGRpbmclM0RzZWxmLnBhZGRpbmclMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSUwQSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGxhYmVsc19iYXRjaCUyMCUzRCUyMHNlbGYucHJvY2Vzc29yLnBhZChsYWJlbHMlM0RsYWJlbF9mZWF0dXJlcyUyQyUyMHBhZGRpbmclM0RzZWxmLnBhZGRpbmclMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSUwQSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMyUyMHJlcGxhY2UlMjBwYWRkaW5nJTIwd2l0aCUyMC0xMDAlMjB0byUyMGlnbm9yZSUyMGxvc3MlMjBjb3JyZWN0bHklMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBsYWJlbHMlMjAlM0QlMjBsYWJlbHNfYmF0Y2glNUIlMjJpbnB1dF9pZHMlMjIlNUQubWFza2VkX2ZpbGwobGFiZWxzX2JhdGNoLmF0dGVudGlvbl9tYXNrLm5lKDEpJTJDJTIwLTEwMCklMEElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBiYXRjaCU1QiUyMmxhYmVscyUyMiU1RCUyMCUzRCUyMGxhYmVscyUwQSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHJldHVybiUyMGJhdGNo",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> dataclasses <span class="hljs-keyword">import</span> dataclass, field
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> typing <span class="hljs-keyword">import</span> <span class="hljs-type">Any</span>, <span class="hljs-type">Dict</span>, <span class="hljs-type">List</span>, <span class="hljs-type">Optional</span>, <span class="hljs-type">Union</span>


<span class="hljs-meta">&gt;&gt;&gt; </span>@dataclass
<span class="hljs-meta">... </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">DataCollatorCTCWithPadding</span>:
<span class="hljs-meta">... </span>    processor: AutoProcessor
<span class="hljs-meta">... </span>    padding: <span class="hljs-type">Union</span>[<span class="hljs-built_in">bool</span>, <span class="hljs-built_in">str</span>] = <span class="hljs-string">&quot;longest&quot;</span>

<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, features: <span class="hljs-type">List</span>[<span class="hljs-type">Dict</span>[<span class="hljs-built_in">str</span>, <span class="hljs-type">Union</span>[<span class="hljs-type">List</span>[<span class="hljs-built_in">int</span>], torch.Tensor]]]</span>) -&gt; <span class="hljs-type">Dict</span>[<span class="hljs-built_in">str</span>, torch.Tensor]:
<span class="hljs-meta">... </span>        <span class="hljs-comment"># split inputs and labels since they have to be of different lengths and need</span>
<span class="hljs-meta">... </span>        <span class="hljs-comment"># different padding methods</span>
<span class="hljs-meta">... </span>        input_features = [{<span class="hljs-string">&quot;input_values&quot;</span>: feature[<span class="hljs-string">&quot;input_values&quot;</span>][<span class="hljs-number">0</span>]} <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features]
<span class="hljs-meta">... </span>        label_features = [{<span class="hljs-string">&quot;input_ids&quot;</span>: feature[<span class="hljs-string">&quot;labels&quot;</span>]} <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features]

<span class="hljs-meta">... </span>        batch = self.processor.pad(input_features, padding=self.padding, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">... </span>        labels_batch = self.processor.pad(labels=label_features, padding=self.padding, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">... </span>        <span class="hljs-comment"># replace padding with -100 to ignore loss correctly</span>
<span class="hljs-meta">... </span>        labels = labels_batch[<span class="hljs-string">&quot;input_ids&quot;</span>].masked_fill(labels_batch.attention_mask.ne(<span class="hljs-number">1</span>), -<span class="hljs-number">100</span>)

<span class="hljs-meta">... </span>        batch[<span class="hljs-string">&quot;labels&quot;</span>] = labels

<span class="hljs-meta">... </span>        <span class="hljs-keyword">return</span> batch`}}),Rs=new V({props:{code:"ZGF0YV9jb2xsYXRvciUyMCUzRCUyMERhdGFDb2xsYXRvckNUQ1dpdGhQYWRkaW5nKHByb2Nlc3NvciUzRHByb2Nlc3NvciUyQyUyMHBhZGRpbmclM0QlMjJsb25nZXN0JTIyKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>data_collator = DataCollatorCTCWithPadding(processor=processor, padding=<span class="hljs-string">&quot;longest&quot;</span>)'}}),Ws=new pt({}),Bs=new V({props:{code:"aW1wb3J0JTIwZXZhbHVhdGUlMEElMEF3ZXIlMjAlM0QlMjBldmFsdWF0ZS5sb2FkKCUyMndlciUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> evaluate

<span class="hljs-meta">&gt;&gt;&gt; </span>wer = evaluate.load(<span class="hljs-string">&quot;wer&quot;</span>)`}}),Xs=new V({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_metrics</span>(<span class="hljs-params">pred</span>):
<span class="hljs-meta">... </span>    pred_logits = pred.predictions
<span class="hljs-meta">... </span>    pred_ids = np.argmax(pred_logits, axis=-<span class="hljs-number">1</span>)

<span class="hljs-meta">... </span>    pred.label_ids[pred.label_ids == -<span class="hljs-number">100</span>] = processor.tokenizer.pad_token_id

<span class="hljs-meta">... </span>    pred_str = processor.batch_decode(pred_ids)
<span class="hljs-meta">... </span>    label_str = processor.batch_decode(pred.label_ids, group_tokens=<span class="hljs-literal">False</span>)

<span class="hljs-meta">... </span>    wer = wer.compute(predictions=pred_str, references=label_str)

<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;wer&quot;</span>: wer}`}}),Ys=new pt({}),os=new Qo({props:{pytorch:!0,tensorflow:!1,jax:!1,$$slots:{pytorch:[tr]},$$scope:{ctx:D}}}),rs=new Sa({props:{$$slots:{default:[ar]},$$scope:{ctx:D}}}),Ns=new pt({}),xs=new V({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset, Audio

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;PolyAI/minds14&quot;</span>, <span class="hljs-string">&quot;en-US&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.cast_column(<span class="hljs-string">&quot;audio&quot;</span>, Audio(sampling_rate=<span class="hljs-number">16000</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate
<span class="hljs-meta">&gt;&gt;&gt; </span>audio_file = dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;path&quot;</span>]`}}),Qs=new V({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBdHJhbnNjcmliZXIlMjAlM0QlMjBwaXBlbGluZSglMjJhdXRvbWF0aWMtc3BlZWNoLXJlY29nbml0aW9uJTIyJTJDJTIwbW9kZWwlM0QlMjJzdGV2aGxpdSUyRm15X2F3ZXNvbWVfYXNyX21pbmRzX21vZGVsJTIyKSUwQXRyYW5zY3JpYmVyKGF1ZGlvX2ZpbGUp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>transcriber = pipeline(<span class="hljs-string">&quot;automatic-speech-recognition&quot;</span>, model=<span class="hljs-string">&quot;stevhliu/my_awesome_asr_minds_model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>transcriber(audio_file)
{<span class="hljs-string">&#x27;text&#x27;</span>: <span class="hljs-string">&#x27;I WOUD LIKE O SET UP JOINT ACOUNT WTH Y PARTNER&#x27;</span>}`}}),is=new Sa({props:{$$slots:{default:[lr]},$$scope:{ctx:D}}}),hs=new Qo({props:{pytorch:!0,tensorflow:!1,jax:!1,$$slots:{pytorch:[or]},$$scope:{ctx:D}}}),{c(){m=n("meta"),y=c(),u=n("h1"),j=n("a"),$=n("span"),g(v.$$.fragment),A=c(),Z=n("span"),k=a("Automatic speech recognition"),E=c(),g(G.$$.fragment),I=c(),g(Q.$$.fragment),B=c(),z=n("p"),P=a("Automatic speech recognition (ASR) converts a speech signal to text, mapping a sequence of audio inputs to text outputs. Virtual assistants like Siri and Alexa use ASR models to help users everyday, and there are many other useful user-facing applications like live captioning and note-taking during meetings."),X=c(),C=n("p"),L=a("This guide will show you how to:"),q=c(),Y=n("ol"),x=n("li"),F=a("Finetune "),N=n("a"),M=a("Wav2Vec2"),R=a(" on the "),H=n("a"),ne=a("MInDS-14"),S=a(" dataset to transcribe audio to text."),O=c(),K=n("li"),ee=a("Use your finetuned model for inference."),T=c(),g(ae.$$.fragment),oe=c(),he=n("p"),$e=a("Before you begin, make sure you have all the necessary libraries installed:"),me=c(),g(re.$$.fragment),ve=c(),ue=n("p"),pe=a("We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:"),Te=c(),g(ie.$$.fragment),Qe=c(),de=n("h2"),fe=n("a"),ye=n("span"),g(ge.$$.fragment),be=c(),Ae=n("span"),Ee=a("Load MInDS-14 dataset"),ze=c(),we=n("p"),Fe=a("Start by loading a smaller subset of the "),le=n("a"),He=a("MInDS-14"),f=a(" dataset from the \u{1F917} Datasets library. This\u2019ll give you a chance to experiment and make sure everything works before spending more time training on the full dataset."),W=c(),g(Me.$$.fragment),ds=c(),je=n("p"),ke=a("Split the dataset\u2019s "),se=n("code"),Fs=a("train"),Hs=a(" split into a train and test set with the "),Se=n("code"),Ss=a("~Dataset.train_test_split"),Ie=a(" method:"),fs=c(),g(Ce.$$.fragment),Ge=c(),Be=n("p"),qe=a("Then take a look at the dataset:"),ys=c(),g(Ms.$$.fragment),Qt=c(),te=n("p"),qa=a("While the dataset contains a lot of useful information, like "),it=n("code"),Da=a("lang_id"),Pa=a(" and "),ct=n("code"),La=a("english_transcription"),Oa=a(", you\u2019ll focus on the "),ht=n("code"),Ka=a("audio"),el=a(" and "),mt=n("code"),sl=a("transcription"),tl=a(" in this guide. Remove the other columns with the "),js=n("a"),al=a("remove_columns"),ll=a(" method:"),zt=c(),g(gs.$$.fragment),Ft=c(),qs=n("p"),nl=a("Take a look at the example again:"),Ht=c(),g(bs.$$.fragment),St=c(),Ds=n("p"),ol=a("There are two fields:"),qt=c(),De=n("ul"),Pe=n("li"),ut=n("code"),rl=a("audio"),pl=a(": a 1-dimensional "),dt=n("code"),il=a("array"),cl=a(" of the speech signal that must be called to load and resample the audio file."),hl=c(),Ps=n("li"),ft=n("code"),ml=a("transcription"),ul=a(": the target text."),Dt=c(),Ve=n("h2"),Le=n("a"),yt=n("span"),g(ws.$$.fragment),dl=c(),Mt=n("span"),fl=a("Preprocess"),Pt=c(),Ls=n("p"),yl=a("The next step is to load a Wav2Vec2 processor to process the audio signal:"),Lt=c(),g(Js.$$.fragment),Ot=c(),Oe=n("p"),Ml=a("The MInDS-14 dataset has a sampling rate of 8000kHz (you can find this information in its "),_s=n("a"),jl=a("dataset card"),gl=a("), which means you\u2019ll need to resample the dataset to 16000kHz to use the pretrained Wav2Vec2 model:"),Kt=c(),g(Us.$$.fragment),ea=c(),Ke=n("p"),bl=a("As you can see in the "),jt=n("code"),wl=a("transcription"),Jl=a(" above, the text contains a mix of upper and lowercase characters. 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