transformers.js/pr_1480/en/_app/immutable/nodes/13.43ee05ab.js

import{s as Mo,n as yo,o as go}from"../chunks/scheduler.6efaaf90.js";import{S as bo,i as wo,e as c,s as r,c as p,h as xo,a as d,d as o,b as l,f as h,g as n,j as i,k as a,l as vo,m as s,n as m,t as f,o as u,p as _}from"../chunks/index.eb3e1f0f.js";import{C as jo,H as T,E as Ho}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.7b6fdc3c.js";import{C as Jo}from"../chunks/CodeBlock.b3d71b16.js";function ko(Gt){let P,Me,Pe,ye,$,ge,M,be,y,we,g,Wt="Processors are used to prepare inputs (e.g., text, image or audio) for a model.",xe,b,Ft="<strong>Example:</strong> Using a <code>WhisperProcessor</code> to prepare an audio input for a model.",ve,w,je,x,Kt='<li><a href="#module_processors">processors</a> <ul><li><em>static</em> <ul><li><a href="#module_processors.Processor">.Processor</a> <ul><li><a href="#new_module_processors.Processor_new"><code>new Processor(config, components, chat_template)</code></a></li> <li><em>instance</em> <ul><li><a href="#module_processors.Processor+image_processor"><code>.image_processor</code></a> ⇒ <code>*</code></li> <li><a href="#module_processors.Processor+tokenizer"><code>.tokenizer</code></a> ⇒ <code>PreTrainedTokenizer</code> | <code>undefined</code></li> <li><a href="#module_processors.Processor+feature_extractor"><code>.feature_extractor</code></a> ⇒ <code>*</code></li> <li><a href="#module_processors.Processor+apply_chat_template"><code>.apply_chat_template(messages, options)</code></a> ⇒ <code>*</code></li> <li><a href="#module_processors.Processor+batch_decode"><code>.batch_decode(...args)</code></a> ⇒ <code>*</code></li> <li><a href="#module_processors.Processor+decode"><code>.decode(...args)</code></a> ⇒ <code>*</code></li> <li><a href="#module_processors.Processor+_call"><code>._call(input, ...args)</code></a> ⇒ <code>Promise.&lt;any&gt;</code></li></ul></li> <li><em>static</em> <ul><li><a href="#module_processors.Processor.from_pretrained"><code>.from_pretrained(pretrained_model_name_or_path, options)</code></a> ⇒ <code>Promise.&lt;Processor&gt;</code></li></ul></li></ul></li></ul></li> <li><em>inner</em> <ul><li><a href="#module_processors..PreTrainedTokenizer"><code>~PreTrainedTokenizer</code></a> : <code>Object</code></li></ul></li></ul></li>',He,Je,ke,v,Le,j,Ce,H,Ot="Represents a Processor that extracts features from an input.",ze,J,St='<strong>Kind</strong>: static class of <a href="#module_processors"><code>processors</code></a>',Ae,k,Vt='<li><a href="#module_processors.Processor">.Processor</a> <ul><li><a href="#new_module_processors.Processor_new"><code>new Processor(config, components, chat_template)</code></a></li> <li><em>instance</em> <ul><li><a href="#module_processors.Processor+image_processor"><code>.image_processor</code></a> ⇒ <code>*</code></li> <li><a href="#module_processors.Processor+tokenizer"><code>.tokenizer</code></a> ⇒ <code>PreTrainedTokenizer</code> | <code>undefined</code></li> <li><a href="#module_processors.Processor+feature_extractor"><code>.feature_extractor</code></a> ⇒ <code>*</code></li> <li><a href="#module_processors.Processor+apply_chat_template"><code>.apply_chat_template(messages, options)</code></a> ⇒ <code>*</code></li> <li><a href="#module_processors.Processor+batch_decode"><code>.batch_decode(...args)</code></a> ⇒ <code>*</code></li> <li><a href="#module_processors.Processor+decode"><code>.decode(...args)</code></a> ⇒ <code>*</code></li> <li><a href="#module_processors.Processor+_call"><code>._call(input, ...args)</code></a> ⇒ <code>Promise.&lt;any&gt;</code></li></ul></li> <li><em>static</em> <ul><li><a href="#module_processors.Processor.from_pretrained"><code>.from_pretrained(pretrained_model_name_or_path, options)</code></a> ⇒ <code>Promise.&lt;Processor&gt;</code></li></ul></li></ul></li>',Ue,Ee,Re,L,Ie,C,Ne,z,qt="Creates a new Processor with the given components",Be,A,eo="<thead><tr><th>Param</th><th>Type</th></tr></thead> <tbody><tr><td>config</td><td><code>Object</code></td> </tr><tr><td>components</td><td><code>Record.&lt;string, Object&gt;</code></td> </tr><tr><td>chat_template</td><td><code>string</code></td></tr></tbody>",Qe,De,Ze,U,Ye,E,Xe,R,to='<strong>Kind</strong>: instance property of <a href="#module_processors.Processor"><code>Processor</code></a><br/> <strong>Returns</strong>: <code>*</code> - The image processor of the processor, if it exists.',Ge,We,Fe,I,Ke,N,Oe,B,oo='<strong>Kind</strong>: instance property of <a href="#module_processors.Processor"><code>Processor</code></a><br/> <strong>Returns</strong>: <code>PreTrainedTokenizer</code> | <code>undefined</code> - The tokenizer of the processor, if it exists.',Se,Ve,qe,Q,et,D,tt,Z,so='<strong>Kind</strong>: instance property of <a href="#module_processors.Processor"><code>Processor</code></a><br/> <strong>Returns</strong>: <code>*</code> - The feature extractor of the processor, if it exists.',ot,st,rt,Y,lt,X,ct,G,ro='<strong>Kind</strong>: instance method of <a href="#module_processors.Processor"><code>Processor</code></a>',dt,W,lo="<thead><tr><th>Param</th><th>Type</th></tr></thead> <tbody><tr><td>messages</td><td><code>*</code></td> </tr><tr><td>options</td><td><code>*</code></td></tr></tbody>",it,at,pt,F,nt,K,mt,O,co='<strong>Kind</strong>: instance method of <a href="#module_processors.Processor"><code>Processor</code></a>',ft,S,io="<thead><tr><th>Param</th><th>Type</th></tr></thead> <tbody><tr><td>...args</td><td><code>*</code></td></tr></tbody>",ut,_t,ht,V,Tt,q,Pt,ee,ao='<strong>Kind</strong>: instance method of <a href="#module_processors.Processor"><code>Processor</code></a>',$t,te,po="<thead><tr><th>Param</th><th>Type</th></tr></thead> <tbody><tr><td>...args</td><td><code>*</code></td></tr></tbody>",Mt,yt,gt,oe,bt,se,wt,re,no="Calls the feature_extractor function with the given input.",xt,le,mo='<strong>Kind</strong>: instance method of <a href="#module_processors.Processor"><code>Processor</code></a><br/> <strong>Returns</strong>: <code>Promise.&lt;any&gt;</code> - A Promise that resolves with the extracted features.',vt,ce,fo="<thead><tr><th>Param</th><th>Type</th><th>Description</th></tr></thead> <tbody><tr><td>input</td><td><code>any</code></td><td><p>The input to extract features from.</p></td> </tr><tr><td>...args</td><td><code>any</code></td><td><p>Additional arguments.</p></td></tr></tbody>",jt,Ht,Jt,de,kt,ie,Lt,ae,uo="Instantiate one of the processor classes of the library from a pretrained model.",Ct,pe,_o=`The processor class to instantiate is selected based on the <code>image_processor_type</code> (or <code>feature_extractor_type</code>; legacy)
property of the config object (either passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible)`,zt,ne,ho='<strong>Kind</strong>: static method of <a href="#module_processors.Processor"><code>Processor</code></a><br/> <strong>Returns</strong>: <code>Promise.&lt;Processor&gt;</code> - A new instance of the Processor class.',At,me,To=`<thead><tr><th>Param</th><th>Type</th><th>Description</th></tr></thead> <tbody><tr><td>pretrained_model_name_or_path</td><td><code>string</code></td><td><p>The name or path of the pretrained model. Can be either:</p> <ul><li>A string, the <em>model id</em> of a pretrained processor hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li> <li>A path to a <em>directory</em> containing processor files, e.g., <code>./my_model_directory/</code>.</li></ul></td> </tr><tr><td>options</td><td><code><a href="#PretrainedProcessorOptions">PretrainedProcessorOptions</a></code></td><td><p>Additional options for loading the processor.</p></td></tr></tbody>`,Ut,Et,Rt,fe,It,ue,Nt,_e,Po="Additional processor-specific properties.",Bt,he,$o='<strong>Kind</strong>: inner typedef of <a href="#module_processors"><code>processors</code></a>',Qt,Dt,Zt,Te,Yt,$e,Xt;return M=new jo({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),y=new T({props:{title:"processors",local:"processors",headingTag:"h1"}}),w=new Jo({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> { <span class="hljs-title class_">AutoProcessor</span>, read_audio } <span class="hljs-keyword">from</span> <span class="hljs-string">&#x27;@huggingface/transformers&#x27;</span>;

<span class="hljs-keyword">const</span> processor = <span class="hljs-keyword">await</span> <span class="hljs-title class_">AutoProcessor</span>.<span class="hljs-title function_">from_pretrained</span>(<span class="hljs-string">&#x27;openai/whisper-tiny.en&#x27;</span>);
<span class="hljs-keyword">const</span> audio = <span class="hljs-keyword">await</span> <span class="hljs-title function_">read_audio</span>(<span class="hljs-string">&#x27;https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac&#x27;</span>, <span class="hljs-number">16000</span>);
<span class="hljs-keyword">const</span> { input_features } = <span class="hljs-keyword">await</span> <span class="hljs-title function_">processor</span>(audio);
<span class="hljs-comment">// Tensor {</span>
<span class="hljs-comment">//   data: Float32Array(240000) [0.4752984642982483, 0.5597258806228638, 0.56434166431427, ...],</span>
<span class="hljs-comment">//   dims: [1, 80, 3000],</span>
<span class="hljs-comment">//   type: &#x27;float32&#x27;,</span>
<span class="hljs-comment">//   size: 240000,</span>
<span class="hljs-comment">// }</span>`,wrap:!1}}),j=new T({props:{title:"processors.Processor",local:"processorsprocessor",headingTag:"h2"}}),C=new T({props:{title:"new Processor(config, components, chat_template)",local:"new-processorconfig-components-chattemplate",headingTag:"h3"}}),E=new T({props:{title:"processor.image_processor ⇒ <code> * </code>",local:"processorimageprocessor--code--code",headingTag:"h3"}}),N=new T({props:{title:"processor.tokenizer ⇒ <code> PreTrainedTokenizer </code> | <code> undefined </code>",local:"processortokenizer--code-pretrainedtokenizer-code--code-undefined-code",headingTag:"h3"}}),D=new T({props:{title:"processor.feature_extractor ⇒ <code> * </code>",local:"processorfeatureextractor--code--code",headingTag:"h3"}}),X=new T({props:{title:"processor.apply_chat_template(messages, options) ⇒ <code> * </code>",local:"processorapplychattemplatemessages-options--code--code",headingTag:"h3"}}),K=new T({props:{title:"processor.batch_decode(...args) ⇒ <code> * 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