README.md

---
base_model: superb/wav2vec2-base-superb-ks
library_name: transformers.js
---

https://huggingface.co/superb/wav2vec2-base-superb-ks with ONNX weights to be compatible with Transformers.js.


## Usage (Transformers.js)

If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```

**Example:** Perform audio classification with `Xenova/wav2vec2-base-superb-ks` and return top 3 results.
```js
import { pipeline } from '@huggingface/transformers';

// Create an audio classification pipeline
const classifier = await pipeline('audio-classification', 'Xenova/wav2vec2-base-superb-ks');

// Predict class
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speech-commands_down.wav';
const output = await classifier(url, { top_k: 3 });
console.log(output);
// [
//   { label: 'down', score: 0.9998697638511658 },
//   { label: 'go', score: 0.00009957332076737657 },
//   { label: '_unknown_', score: 0.000029320701287360862 },
// ]
```

---

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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).