metadata
base_model: facebook/mms-300m
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 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).