import{s as Ms,n as ns,o as ps}from"../chunks/scheduler.cd324960.js";import{S as is,i as js,e as n,s as a,c as j,h as ys,a as p,d as t,b as M,f as as,g as y,j as i,k as Ql,l as os,m as e,n as o,t as u,o as m,p as c}from"../chunks/index.b5b48820.js";import{C as us,H as ms,E as cs}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.723fcaac.js";import{C as U}from"../chunks/CodeBlock.d3453e39.js";function Us(Wl){let r,K,P,ll,J,sl,T,tl,w,ql=`이 코스에서 우리는 🤗 Datasets 라이브러리를 사용하여 오디오 데이터셋을 다룹니다.🤗 Datasets는 오디오를 포함한 모든 양식의 데이터셋을 다운로드하고 준비할 수 있는 오픈소스 라이브러리입니다.
이 라이브러리는 허깅페이스 허브에서 공개적으로 사용 가능한 머신러닝 데이터셋에 쉽게 접근할 수 있도록 도와줍니다. 무엇보다도, 🤗 Datasets는 연구자와 실무자 모두가 오디오 데이터셋을 쉽게 다룰 수 있도록 만드는 특화 기능들을 여럿 가지고 있습니다.`,el,h,xl="시작에 앞서, 🤗 Datasets 라이브러리가 설치돼있는지 확인해주세요:",al,A,Ml,f,Rl="🤗 Datasets의 주된 기능 중 하나는 <code>load_dataset()</code> 파이썬 코드 한줄로 데이터셋을 다운로드하고 준비할 수 있다는것입니다.",nl,C,Fl='<a href="https://huggingface.co/datasets/PolyAI/minds14" rel="nofollow">MINDS-14</a>라고 하는 오디오 데이터셋을 불러와 다뤄봅시다. 이 데이터셋은 여러 언어와 방언으로 사람들이 인터넷뱅킹에 대해 묻는 내용이 녹음돼있습니다.',pl,b,Yl="MINDS-14 데이터셋을 불러오기 위해 허브에서의 데이터셋 식별자인 (<code>PolyAI/minds14</code>)를 <code>load_dataset</code> 함수로 넘겨줘야 합니다. 또한 데이터셋의 호주 부분(<code>en-AU</code>)에만 관심이 있고 학습용 데이터에만 관심이 있음을 명시하겠습니다.:",il,I,jl,$,Hl="<strong>Output:</strong>",yl,g,ol,k,Sl="데이터셋은 654개의 오디오 파일로 이뤄져있습니다. 각각의 파일들은 자막, 영어 번역, 그리고 질문자의 의도를 나타내는 레이블(label)이 함께 제공됩니다. 오디오 컬럼(column)에는 원시(raw) 오디오 데이터가 포함되어 있습니다. 예제를 하나 살펴보겠습니다:",ul,G,ml,v,Nl="<strong>Output:</strong>",cl,Z,Ul,E,Xl="오디오 컬럼에 여러 feature가 있음을 알 수 있습니다. 각각은 다음과 같습니다:",rl,B,zl="<li><code>path</code>: 오디오 파일의 경로(이 예제의 경우 <code>*.wav</code>).</li> <li><code>array</code>: 1차원 넘파이 배열로 디코딩된 오디오 데이터.</li> <li><code>sampling_rate</code>: 오디오 파일의 샘플링 속도(이 예제의 경우 8,000 Hz).</li>",dl,_,Vl="<code>intent_class</code>는 오디오 녹음이 어떤 카테고리로 분류되는지 나타냅니다. 이 숫자를 의미있는 문자열로 바꾸려면 <code>int2str()</code> 메소드를 사용하세요:",Jl,Q,Tl,W,Ll="<strong>Output:</strong>",wl,q,hl,x,Dl="<code>transcription</code> feature를 보면 누군가가 청구서를 지불하는 것에 대해 질문하는 녹음의 오디오 파일이란것을 알 수 있습니다.",Al,R,Pl="이 데이터의 부분집합에 대해 오디오 분류기(classifier)를 학습시킬 계획이시라면, 이 모든 feature가 필요하진 않을지도 모릅니다. 예를 들어, <code>lang_id</code>는 모든 예제에서 같은 값을 지니기 때문에 그다지 쓸모 있지 않을겁니다.  <code>english_transcription</code>는 이 호주데이터에선 대부분이 <code>transcription</code>과 같을테니 삭제해도 좋을겁니다.",fl,F,Ol="이런 관계없는 feature는 🤗 Datasets의 <code>remove_columns</code> 메소드를 사용하면 쉽게 제거할 수 있습니다:",Cl,Y,bl,H,Kl="<strong>Output:</strong>",Il,S,$l,N,ls=`이제 데이터셋의 원시 컨텐츠들을 불러오고 살펴봤으니, 몇개를 들어봅시다!
<code>Gradio</code>의 <code>Blocks</code>와 <code>Audio</code> 기능을 써서 데이터셋의 샘플 몇개를 무작위로 디코딩해보겠습니다:`,gl,X,kl,z,ss="원하신다면, 예제 몇개를 시각화해보는 것도 좋습니다. 첫번째 예제의 파형을 그려보겠습니다.",Gl,V,vl,d,ts='<img src="https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/waveform_unit1.png" alt="Waveform plot"/>',Zl,L,es='한번 해보세요! MINDS-14 데이터셋의 다른 언어나 방언을 다운로드하여 듣고 시각화를 해보세요. 전체 데이터셋이 어떨지 대략적인 감을 줄겁니다. <a href="https://huggingface.co/datasets/PolyAI/minds14" rel="nofollow">여기</a>서 가능한 언어들이 무엇인지 확인하실 수 있습니다.',El,D,Bl,O,_l;return J=new us({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),T=new ms({props:{title:"오디오 데이터셋 불러오기 및 탐색하기",local:"load-and-explore-an-audio-dataset",headingTag:"h1"}}),A=new U({props:{code:"cGlwJTIwaW5zdGFsbCUyMGRhdGFzZXRzJTVCYXVkaW8lNUQ=",highlighted:"pip install datasets[audio]",wrap:!1}}),I=new U({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBbWluZHMlMjAlM0QlMjBsb2FkX2RhdGFzZXQoJTIyUG9seUFJJTJGbWluZHMxNCUyMiUyQyUyMG5hbWUlM0QlMjJlbi1BVSUyMiUyQyUyMHNwbGl0JTNEJTIydHJhaW4lMjIpJTBBbWluZHM=",highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

minds = load_dataset(<span class="hljs-string">&quot;PolyAI/minds14&quot;</span>, name=<span class="hljs-string">&quot;en-AU&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
minds`,wrap:!1}}),g=new U({props:{code:"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",highlighted:`Dataset(
    <span class="hljs-punctuation">{</span>
<span class="hljs-symbol">        features:</span> [
            <span class="hljs-string">&quot;path&quot;</span>,
            <span class="hljs-string">&quot;audio&quot;</span>,
            <span class="hljs-string">&quot;transcription&quot;</span>,
            <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>,
        ],
<span class="hljs-symbol">        num_rows:</span> <span class="hljs-number">654</span>,
    <span class="hljs-punctuation">}</span>
)`,wrap:!1}}),G=new U({props:{code:"ZXhhbXBsZSUyMCUzRCUyMG1pbmRzJTVCMCU1RCUwQWV4YW1wbGU=",highlighted:`example = minds[<span class="hljs-number">0</span>]
example`,wrap:!1}}),Z=new U({props:{code:"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",highlighted:`{
    <span class="hljs-comment">&quot;path&quot;</span>: <span class="hljs-comment">&quot;/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-AU~PAY_BILL/response_4.wav&quot;</span>,
    <span class="hljs-comment">&quot;audio&quot;</span>: {
        <span class="hljs-comment">&quot;path&quot;</span>: <span class="hljs-comment">&quot;/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-AU~PAY_BILL/response_4.wav&quot;</span>,
        <span class="hljs-comment">&quot;array&quot;</span>: array(
            [<span class="hljs-number">0.0</span>, <span class="hljs-number">0.00024414</span>, <span class="hljs-number">-0.00024414</span>, ..., <span class="hljs-number">-0.00024414</span>, <span class="hljs-number">0.00024414</span>, <span class="hljs-number">0.0012207</span>],
            dtype=float32,
        ),
        <span class="hljs-comment">&quot;sampling_rate&quot;</span>: <span class="hljs-number">8000</span>,
    },
    <span class="hljs-comment">&quot;transcription&quot;</span>: <span class="hljs-comment">&quot;I would like to pay my electricity bill using my card can you please assist&quot;</span>,
    <span class="hljs-comment">&quot;english_transcription&quot;</span>: <span class="hljs-comment">&quot;I would like to pay my electricity bill using my card can you please assist&quot;</span>,
    <span class="hljs-comment">&quot;intent_class&quot;</span>: <span class="hljs-number">13</span>,
    <span class="hljs-comment">&quot;lang_id&quot;</span>: <span class="hljs-number">2</span>,
}`,wrap:!1}}),Q=new U({props:{code:"aWQybGFiZWwlMjAlM0QlMjBtaW5kcy5mZWF0dXJlcyU1QiUyMmludGVudF9jbGFzcyUyMiU1RC5pbnQyc3RyJTBBaWQybGFiZWwoZXhhbXBsZSU1QiUyMmludGVudF9jbGFzcyUyMiU1RCk=",highlighted:`id2label = minds.features[<span class="hljs-string">&quot;intent_class&quot;</span>].int2str
id2label(example[<span class="hljs-string">&quot;intent_class&quot;</span>])`,wrap:!1}}),q=new U({props:{code:"JTIycGF5X2JpbGwlMjI=",highlighted:'<span class="hljs-string">&quot;pay_bill&quot;</span>',wrap:!1}}),Y=new U({props:{code:"Y29sdW1uc190b19yZW1vdmUlMjAlM0QlMjAlNUIlMjJsYW5nX2lkJTIyJTJDJTIwJTIyZW5nbGlzaF90cmFuc2NyaXB0aW9uJTIyJTVEJTBBbWluZHMlMjAlM0QlMjBtaW5kcy5yZW1vdmVfY29sdW1ucyhjb2x1bW5zX3RvX3JlbW92ZSklMEFtaW5kcw==",highlighted:`columns_to_remove = [<span class="hljs-string">&quot;lang_id&quot;</span>, <span class="hljs-string">&quot;english_transcription&quot;</span>]
minds = minds.remove_columns(columns_to_remove)
minds`,wrap:!1}}),S=new U({props:{code:"RGF0YXNldCglN0JmZWF0dXJlcyUzQSUyMCU1QiUyMnBhdGglMjIlMkMlMjAlMjJhdWRpbyUyMiUyQyUyMCUyMnRyYW5zY3JpcHRpb24lMjIlMkMlMjAlMjJpbnRlbnRfY2xhc3MlMjIlNUQlMkMlMjBudW1fcm93cyUzQSUyMDY1NCU3RCk=",highlighted:'<span class="hljs-constructor">Dataset({<span class="hljs-params">features</span>: [<span class="hljs-string">&quot;path&quot;</span>, <span class="hljs-string">&quot;audio&quot;</span>, <span class="hljs-string">&quot;transcription&quot;</span>, <span class="hljs-string">&quot;intent_class&quot;</span>], <span class="hljs-params">num_rows</span>: 654})</span>',wrap:!1}}),X=new U({props:{code:"aW1wb3J0JTIwZ3JhZGlvJTIwYXMlMjBnciUwQSUwQSUwQWRlZiUyMGdlbmVyYXRlX2F1ZGlvKCklM0ElMEElMjAlMjAlMjAlMjBleGFtcGxlJTIwJTNEJTIwbWluZHMuc2h1ZmZsZSgpJTVCMCU1RCUwQSUyMCUyMCUyMCUyMGF1ZGlvJTIwJTNEJTIwZXhhbXBsZSU1QiUyMmF1ZGlvJTIyJTVEJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwKCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGF1ZGlvJTVCJTIyc2FtcGxpbmdfcmF0ZSUyMiU1RCUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGF1ZGlvJTVCJTIyYXJyYXklMjIlNUQlMkMlMEElMjAlMjAlMjAlMjApJTJDJTIwaWQybGFiZWwoZXhhbXBsZSU1QiUyMmludGVudF9jbGFzcyUyMiU1RCklMEElMEElMEF3aXRoJTIwZ3IuQmxvY2tzKCklMjBhcyUyMGRlbW8lM0ElMEElMjAlMjAlMjAlMjB3aXRoJTIwZ3IuQ29sdW1uKCklM0ElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBmb3IlMjBfJTIwaW4lMjByYW5nZSg0KSUzQSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGF1ZGlvJTJDJTIwbGFiZWwlMjAlM0QlMjBnZW5lcmF0ZV9hdWRpbygpJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwb3V0cHV0JTIwJTNEJTIwZ3IuQXVkaW8oYXVkaW8lMkMlMjBsYWJlbCUzRGxhYmVsKSUwQSUwQWRlbW8ubGF1bmNoKGRlYnVnJTNEVHJ1ZSk=",highlighted:`<span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr


<span class="hljs-keyword">def</span> <span class="hljs-title function_">generate_audio</span>():
    example = minds.shuffle()[<span class="hljs-number">0</span>]
    audio = example[<span class="hljs-string">&quot;audio&quot;</span>]
    <span class="hljs-keyword">return</span> (
        audio[<span class="hljs-string">&quot;sampling_rate&quot;</span>],
        audio[<span class="hljs-string">&quot;array&quot;</span>],
    ), id2label(example[<span class="hljs-string">&quot;intent_class&quot;</span>])


<span class="hljs-keyword">with</span> gr.Blocks() <span class="hljs-keyword">as</span> demo:
    <span class="hljs-keyword">with</span> gr.Column():
        <span class="hljs-keyword">for</span> _ <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">4</span>):
            audio, label = generate_audio()
            output = gr.Audio(audio, label=label)

demo.launch(debug=<span class="hljs-literal">True</span>)`,wrap:!1}}),V=new U({props:{code:"aW1wb3J0JTIwbGlicm9zYSUwQWltcG9ydCUyMG1hdHBsb3RsaWIucHlwbG90JTIwYXMlMjBwbHQlMEFpbXBvcnQlMjBsaWJyb3NhLmRpc3BsYXklMEElMEFhcnJheSUyMCUzRCUyMGV4YW1wbGUlNUIlMjJhdWRpbyUyMiU1RCU1QiUyMmFycmF5JTIyJTVEJTBBc2FtcGxpbmdfcmF0ZSUyMCUzRCUyMGV4YW1wbGUlNUIlMjJhdWRpbyUyMiU1RCU1QiUyMnNhbXBsaW5nX3JhdGUlMjIlNUQlMEElMEFwbHQuZmlndXJlKCkuc2V0X2ZpZ3dpZHRoKDEyKSUwQWxpYnJvc2EuZGlzcGxheS53YXZlc2hvdyhhcnJheSUyQyUyMHNyJTNEc2FtcGxpbmdfcmF0ZSk=",highlighted:`<span class="hljs-keyword">import</span> librosa
<span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-keyword">import</span> librosa.display

array = example[<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>]
sampling_rate = example[<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;sampling_rate&quot;</span>]

plt.figure().set_figwidth(<span class="hljs-number">12</span>)
librosa.display.waveshow(array, sr=sampling_rate)`,wrap:!1}}),D=new 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