import{s as Q,n as O,o as V}from"../chunks/scheduler.cd324960.js";import{S as K,i as tt,e as o,s as a,c as L,h as et,a as p,d as n,b as l,f as X,g as W,j as w,k as Y,l as nt,m as s,n as H,t as S,o as k,p as z}from"../chunks/index.d5c3adcc.js";import{C as st,H as at,E as lt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.87c38818.js";import{C as it}from"../chunks/CodeBlock.2ff0aef1.js";function ot(F){let i,g,y,C,m,I,r,M,u,N=`在本单元中，我们探讨了微调 ASR 模型的挑战，验证了在新语言上微调像 Whisper（即使是小型检查点）模型需要不少时间和资源。
为了提供实践经验，我们设计了一个练习，让您能够在使用较小数据集的情况下，体验微调 ASR 模型的过程。
这个练习的主要目的是让您熟悉过程，而不是取得生产级别的结果。我们故意设置了一个低指标，以确保即使在资源有限的情况下，您也应该能够达到它。`,x,c,A="以下是指导说明：",b,d,G="<li>使用 <code>&quot;PolyAI/minds14&quot;</code> 数据集的美式英语（“en-US”）子集，微调 <code>&quot;openai/whisper-tiny&quot;</code> 模型。</li> <li>使用 <strong>前450 个样本</strong>进行训练，其余的用于评估。确保在使用 <code>.map</code> 方法预处理数据集时设置 <code>num_proc=1</code>（这将确保您的模型正确提交以进行评估）。</li> <li>评估模型时，使用本单元描述的 <code>wer</code> 和 <code>wer_ortho</code> 指标。但是，<em>不要</em> 通过乘以 100 将指标转换为百分比（例如，如果 WER 是 42%，我们将期望在此练习中看到 0.42 的值）。</li>",q,f,R="您微调了一个模型后，请使用以下 <code>kwargs</code> 将其上传到 🤗 Hub：",v,T,P,J,Z="如果您的模型的标准化 WER（<code>wer</code>）低于 <strong>0.37</strong>，您将通过这个任务。",U,$,D="请随意构建您的模型 demo，并在 Discord 上分享！如果您有任何问题，欢迎在 #audio-study-group 频道发布。",j,h,B,_,E;return m=new st({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),r=new at({props:{title:"实战练习",local:"实战练习",headingTag:"h1"}}),T=new it({props:{code:"a3dhcmdzJTIwJTNEJTIwJTdCJTBBJTIwJTIwJTIwJTIwJTIyZGF0YXNldF90YWdzJTIyJTNBJTIwJTIyUG9seUFJJTJGbWluZHMxNCUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMmZpbmV0dW5lZF9mcm9tJTIyJTNBJTIwJTIyb3BlbmFpJTJGd2hpc3Blci10aW55JTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIydGFza3MlMjIlM0ElMjAlMjJhdXRvbWF0aWMtc3BlZWNoLXJlY29nbml0aW9uJTIyJTJDJTBBJTdE",highlighted:`<span class="hljs-attribute">kwargs</span> = {
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