Xenova/hubert-base-ls960

https://huggingface.co/facebook/hubert-base-ls960 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 HubertModel for feature extraction.

import { AutoProcessor, AutoModel, read_audio } from '@xenova/transformers';

// Read and preprocess audio
const processor = await AutoProcessor.from_pretrained('Xenova/hubert-base-ls960');
const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav', 16000);
const inputs = await processor(audio);

// Load and run model with inputs
const model = await AutoModel.from_pretrained('Xenova/hubert-base-ls960');
const output = await model(inputs);
// {
//   last_hidden_state: Tensor {
//     dims: [ 1, 549, 768 ],
//     type: 'float32',
//     data: Float32Array(421632) [0.0682469978928566, 0.08104046434164047, -0.4975186586380005, ...],
//     size: 421632
//   }
// }

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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).