audio-course/pr_201/en/chapter5/hands_on.html

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		<link rel="modulepreload" href="/docs/audio-course/pr_201/en/_app/immutable/chunks/EditOnGithub.5a9bb8c5.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Hands-on exercise&quot;,&quot;local&quot;:&quot;hands-on-exercise&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <h1 class="relative group"><a id="hands-on-exercise" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#hands-on-exercise"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Hands-on exercise</span></h1> <p data-svelte-h="svelte-x2tvlr">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.</p> <p data-svelte-h="svelte-acxoy6">Here are the instructions:</p> <ul data-svelte-h="svelte-14v5kzw"><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></ul> <p data-svelte-h="svelte-1p3fyil">Once you have fine-tuned a model, make sure to upload it to the 🤗 Hub with the following <code>kwargs</code>:</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-attribute">kwargs</span> = {
     <span class="hljs-string">&quot;dataset_tags&quot;</span>: <span class="hljs-string">&quot;PolyAI/minds14&quot;</span>,
    <span class="hljs-string">&quot;finetuned_from&quot;</span>: <span class="hljs-string">&quot;openai/whisper-tiny&quot;</span>,
    <span class="hljs-string">&quot;tasks&quot;</span>: <span class="hljs-string">&quot;automatic-speech-recognition&quot;</span>,
}<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-i4linb">You will pass this assignment if your model’s normalised WER (<code>wer</code>) is lower than <strong>0.37</strong>.</p> <p data-svelte-h="svelte-1vc7edk">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.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/audio-transformers-course/blob/main/chapters/en/chapter5/hands_on.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1">&lt;</span> <span data-svelte-h="svelte-x0xyl0">&gt;</span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a>  <p></p> 
			
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