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

https://huggingface.co/superb/hubert-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/@xenova/transformers) using:
```bash
npm i @xenova/transformers
```

**Example:** Speech command recognition w/ `Xenova/hubert-base-superb-ks`.

```javascript
import { pipeline } from '@xenova/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, { topk: 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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).