import { pipeline } from '@huggingface/transformers';

// Create audio classification pipeline
const classifier = await pipeline('audio-classification', 'Xenova/hubert-base-superb-ks');

// Classify audio
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speech-commands_down.wav';
const output = await classifier(url, { top_k: 5 });
// [
//   { label: 'down', score: 0.9954305291175842 },
//   { label: 'go', score: 0.004518700763583183 },
//   { label: '_unknown_', score: 0.00005029444946558215 },
//   { label: 'no', score: 4.877569494965428e-7 },
//   { label: 'stop', score: 5.504634081887616e-9 }
// ]

Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).