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--- |
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base_model: superb/hubert-base-superb-ks |
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library_name: transformers.js |
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--- |
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https://huggingface.co/superb/hubert-base-superb-ks with ONNX weights to be compatible with Transformers.js. |
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## Usage (Transformers.js) |
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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: |
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```bash |
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npm i @xenova/transformers |
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``` |
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**Example:** Speech command recognition w/ `Xenova/hubert-base-superb-ks`. |
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```javascript |
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import { pipeline } from '@xenova/transformers'; |
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// Create audio classification pipeline |
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const classifier = await pipeline('audio-classification', 'Xenova/hubert-base-superb-ks'); |
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// Classify audio |
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const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speech-commands_down.wav'; |
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const output = await classifier(url, { topk: 5 }); |
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// [ |
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// { label: 'down', score: 0.9954305291175842 }, |
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// { label: 'go', score: 0.004518700763583183 }, |
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// { label: '_unknown_', score: 0.00005029444946558215 }, |
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// { label: 'no', score: 4.877569494965428e-7 }, |
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// { label: 'stop', score: 5.504634081887616e-9 } |
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// ] |
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``` |
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--- |
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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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |
