import{s as R,n as Y,o as B}from"../chunks/scheduler.f7e1785c.js";import{S as D,i as F,g as p,s as o,r as M,A as G,h as f,f as a,c as s,j as S,u as z,x as P,k as U,y as N,a as n,v as W,d as j,t as q,w as I}from"../chunks/index.9f8f0838.js";import{H as O,E as J}from"../chunks/EditOnGithub.5a9bb8c5.js";function K(C){let i,w,h,b,r,v,l,A=`Welcome to the second unit of the Hugging Face audio course! Previously, we explored the fundamentals of audio data
and learned how to work with audio datasets using the 🤗 Datasets and 🤗 Transformers libraries. We discussed various
concepts such as sampling rate, amplitude, bit depth, waveform, and spectrograms, and saw how to preprocess data to
prepare it for a pre-trained model.`,y,c,H=`At this point you may be eager to learn about the audio tasks that 🤗 Transformers can handle, and you have all the foundational
knowledge necessary to dive in! Let’s take a look at some of the mind-blowing audio task examples:`,x,u,L=`<li><strong>Audio classification</strong>: easily categorize audio clips into different categories. You can identify whether a recording
is of a barking dog or a meowing cat, or what music genre a song belongs to.</li> <li><strong>Automatic speech recognition</strong>: transform audio clips into text by transcribing them automatically. You can get a text
representation of a recording of someone speaking, like “How are you doing today?“. Rather useful for note taking!</li> <li><strong>Speaker diarization</strong>: Ever wondered who’s speaking in a recording? With 🤗 Transformers, you can identify which speaker
is talking at any given time in an audio clip. Imagine being able to differentiate between “Alice” and “Bob” in a recording
of them having a conversation.</li> <li><strong>Text to speech</strong>: create a narrated version of a text that can be used to produce an audio book, help with accessibility,
or give a voice to an NPC in a game. With 🤗 Transformers, you can easily do that!</li>`,_,d,E=`In this unit, you’ll learn how to use pre-trained models for some of these tasks using the <code>pipeline()</code> function from 🤗 Transformers.
Specifically, we’ll see how the pre-trained models can be used for audio classification, automatic speech recognition and audio generation.
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