import{s as D,n as X,o as O}from"../chunks/scheduler.f7e1785c.js";import{S as Q,i as V,g as l,s as a,r as P,A as K,h as r,f as s,c as i,j as Z,u as k,x as g,k as Y,y as ee,a as n,v as B,d as W,t as j,w as F}from"../chunks/index.9f8f0838.js";import{C as te}from"../chunks/CodeBlock.4baef5d7.js";import{H as se,E as ne}from"../chunks/getInferenceSnippets.ba5f4cd0.js";function ae(L){let o,v,y,x,u,_,m,A=`In this unit, we explored the challenges of fine-tuning ASR models, acknowledging the time and resources required to
fine-tune a model like Whisper (even a small checkpoint) on a new language. To provide a hands-on experience, we have
designed an exercise that allows you to navigate the process of fine-tuning an ASR model while using a smaller dataset.
The main goal of this exercise is to familiarize you with the process rather than expecting production-level results.
We have intentionally set a low metric to ensure that even with limited resources, you should be able to achieve it.`,I,c,S="Here are the instructions:",$,d,z="<li>Fine-tune the <code>”openai/whisper-tiny”</code> model using the American English (“en-US”) subset of the <code>”PolyAI/minds14”</code> dataset.</li> <li>Use the first <strong>450 examples for training</strong>, and the rest for evaluation. Ensure you set <code>num_proc=1</code> when pre-processing the dataset using the <code>.map</code> method (this will ensure your model is submitted correctly for assessment).</li> <li>To evaluate the model, use the <code>wer</code> and <code>wer_ortho</code> metrics as described in this Unit. However, <em>do not</em> convert the metric into percentages by multiplying by 100 (E.g. if WER is 42%, we’ll expect to see the value of 0.42 in this exercise).</li>",b,p,G="Once you have fine-tuned a model, make sure to upload it to the 🤗 Hub with the following <code>kwargs</code>:",C,h,M,f,N="You will pass this assignment if your model’s normalised WER (<code>wer</code>) is lower than <strong>0.37</strong>.",q,w,R="Feel free to build a demo of your model, and share it on Discord! If you have questions, post them in the #audio-study-group channel.",U,T,E,J,H;return u=new se({props:{title:"Hands-on exercise",local:"hands-on-exercise",headingTag:"h1"}}),h=new te({props:{code:"a3dhcmdzJTIwJTNEJTIwJTdCJTBBJTIwJTIwJTIwJTIwJTIwJTIyZGF0YXNldF90YWdzJTIyJTNBJTIwJTIyUG9seUFJJTJGbWluZHMxNCUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMmZpbmV0dW5lZF9mcm9tJTIyJTNBJTIwJTIyb3BlbmFpJTJGd2hpc3Blci10aW55JTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIydGFza3MlMjIlM0ElMjAlMjJhdXRvbWF0aWMtc3BlZWNoLXJlY29nbml0aW9uJTIyJTJDJTBBJTdE",highlighted:`<span class="hljs-attribute">kwargs</span> = {
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    <span class="hljs-string">&quot;tasks&quot;</span>: <span class="hljs-string">&quot;automatic-speech-recognition&quot;</span>,
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