---
library_name: "transformers.js"
tags:
 - mms
 - feature-extraction
---

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`).