| base_model: hf-audio/wav2vec2-bert-CV16-en | |
| library_name: transformers.js | |
| https://huggingface.co/hf-audio/wav2vec2-bert-CV16-en 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/@huggingface/transformers) using: | |
| ```bash | |
| npm i @huggingface/transformers | |
| ``` | |
| You can then use the model for speech recognition with: | |
| ```js | |
| import { pipeline } from '@huggingface/transformers'; | |
| // Create an Automatic Speech Recognition pipeline | |
| const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/wav2vec2-bert-CV16-en'); | |
| // Transcribe audio | |
| const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav'; | |
| const output = await transcriber(url); | |
| // { text: 'and so my fellow americans ask not what your country can do for you ask what you can do for your country' } | |
| ``` | |
| --- | |
| 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`). |