import{S as cs,i as is,s as us,e as t,k as u,w as x,t as n,M as ds,c as o,d as s,m as d,a as r,x as P,h as l,b as c,N as ps,G as a,g as i,y as z,L as hs,q as S,o as T,B as C,v as fs}from"../chunks/vendor-hf-doc-builder.js";import{I as Ia}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as ge}from"../chunks/CodeBlock-hf-doc-builder.js";function ms(Na){let _,ve,g,y,le,D,Be,pe,Je,ke,Q,Ke,je,b,ce,Ve,Qe,I,Xe,X,Ye,Ze,ye,$,ea,N,aa,sa,be,v,w,ie,F,ta,ue,oa,$e,f,ra,Y,na,la,Z,pa,ca,we,L,Ae,ee,ia,Ee,U,qe,k,ae,Fa,ua,se,La,xe,j,A,de,M,da,he,ha,Pe,h,fa,te,ma,_a,O,ga,va,H,ka,ja,ze,E,W,G,ya,fe,ba,$a,wa,R,Aa,B,J,Ea,me,qa,xa,Pa,K,Se,m,za,oe,Sa,Ta,_e,Ca,Da,Te,V,Ce;return D=new Ia({}),F=new Ia({}),L=new ge({props:{code:`from datasets import load_dataset, Audio

dataset = load_dataset("PolyAI/minds14", "en-US", split="train")
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset, Audio

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;PolyAI/minds14&quot;</span>, <span class="hljs-string">&quot;en-US&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.cast_column(<span class="hljs-string">&quot;audio&quot;</span>, Audio(sampling_rate=<span class="hljs-number">16000</span>))`}}),U=new ge({props:{code:'dataset[0]["audio"]',highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>]
{<span class="hljs-string">&#x27;array&#x27;</span>: array([ <span class="hljs-number">2.3443763e-05</span>,  <span class="hljs-number">2.1729663e-04</span>,  <span class="hljs-number">2.2145823e-04</span>, ...,
         <span class="hljs-number">3.8356509e-05</span>, -<span class="hljs-number">7.3497440e-06</span>, -<span class="hljs-number">2.1754686e-05</span>], dtype=float32),
 <span class="hljs-string">&#x27;path&#x27;</span>: <span class="hljs-string">&#x27;/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav&#x27;</span>,
 <span class="hljs-string">&#x27;sampling_rate&#x27;</span>: <span class="hljs-number">16000</span>}`}}),M=new Ia({}),R=new ge({props:{code:`from transformers import AutoTokenizer, AutoFeatureExtractor, AutoProcessor

model_checkpoint = "facebook/wav2vec2-large-xlsr-53"
tokenizer = AutoTokenizer("./vocab.json", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")
feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)
processor = AutoProcessor.from_pretrained(feature_extractor=feature_extractor, tokenizer=tokenizer)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoFeatureExtractor, AutoProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>model_checkpoint = <span class="hljs-string">&quot;facebook/wav2vec2-large-xlsr-53&quot;</span>
<span class="hljs-comment"># after defining a vocab.json file you can instantiate a tokenizer object:</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer(<span class="hljs-string">&quot;./vocab.json&quot;</span>, unk_token=<span class="hljs-string">&quot;[UNK]&quot;</span>, pad_token=<span class="hljs-string">&quot;[PAD]&quot;</span>, word_delimiter_token=<span class="hljs-string">&quot;|&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(feature_extractor=feature_extractor, tokenizer=tokenizer)`}}),K=new ge({props:{code:`from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)`}}),V=new ge({props:{code:`def prepare_dataset(batch):
    audio = batch["audio"]
    batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
    batch["input_length"] = len(batch["input_values"])
    with processor.as_target_processor():
        batch["labels"] = processor(batch["sentence"]).input_ids
    return batch
dataset = dataset.map(prepare_dataset, remove_columns=dataset.column_names)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">prepare_dataset</span>(<span class="hljs-params">batch</span>):
<span class="hljs-meta">... </span>    audio = batch[<span class="hljs-string">&quot;audio&quot;</span>]
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;input_values&quot;</span>] = processor(audio[<span class="hljs-string">&quot;array&quot;</span>], sampling_rate=audio[<span class="hljs-string">&quot;sampling_rate&quot;</span>]).input_values[<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;input_length&quot;</span>] = <span class="hljs-built_in">len</span>(batch[<span class="hljs-string">&quot;input_values&quot;</span>])
<span class="hljs-meta">... </span>    <span class="hljs-keyword">with</span> processor.as_target_processor():
<span class="hljs-meta">... </span>        batch[<span class="hljs-string">&quot;labels&quot;</span>] = processor(batch[<span class="hljs-string">&quot;sentence&quot;</span>]).input_ids
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> batch
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.<span class="hljs-built_in">map</span>(prepare_dataset, remove_columns=dataset.column_names)`}}),{c(){_=t("meta"),ve=u(),g=t("h1"),y=t("a"),le=t("span"),x(D.$$.fragment),Be=u(),pe=t("span"),Je=n("Process audio data"),ke=u(),Q=t("p"),Ke=n("This guide shows specific methods for processing audio datasets. Learn how to:"),je=u(),b=t("ul"),ce=t("li"),Ve=n("Resample the sampling rate."),Qe=u(),I=t("li"),Xe=n("Use "),X=t("a"),Ye=n("map()"),Ze=n(" with audio datasets."),ye=u(),$=t("p"),ea=n("For a guide on how to process any type of dataset, take a look at the "),N=t("a"),aa=n("general process guide"),sa=n("."),be=u(),v=t("h2"),w=t("a"),ie=t("span"),x(F.$$.fragment),ta=u(),ue=t("span"),oa=n("Cast"),$e=u(),f=t("p"),ra=n("The "),Y=t("a"),na=n("cast_column()"),la=n(" function is used to cast a column to another feature to be decoded. 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_s={local:"process-audio-data",sections:[{local:"cast",title:"Cast"},{local:"map",title:"Map"}],title:"Process audio data"};function gs(Na){return fs(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ys extends cs{constructor(_){super();is(this,_,gs,ms,us,{})}}export{ys as default,_s as metadata};
