mms-300m

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 JavaScript library from NPM using:

npm i @xenova/transformers

Example: Load and run a Wav2Vec2Model for feature extraction.

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 and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).