Update README.md

README.md

@@ -4,4 +4,32 @@ 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`).