Update README.md

README.md

@@ -7,4 +7,37 @@ tags:
 
 https://huggingface.co/facebook/mms-300m 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:** Load and run a `Wav2Vec2Model` for feature extraction.
+
+```js
+import { AutoProcessor, AutoModel, read_audio } from '@xenova/transformers';
+
+// Read and preprocess audio
+const processor = await AutoProcessor.from_pretrained('Xenova/mms-300m');
+const audio = await read_audio('https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac', 16000);
+const inputs = await processor(audio);
+
+// Run model with inputs
+const model = await AutoModel.from_pretrained('Xenova/mms-300m');
+const output = await model(inputs);
+// {
+//   last_hidden_state: Tensor {
+//     dims: [ 1, 1144, 1024 ],
+//     type: 'float32',
+//     data: Float32Array(1171456) [ ... ],
+//     size: 1171456
+//   }
+// }
+```
+
+---
+
+
 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`).