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Xenova/whisper-tiny.en
whisper-tiny.en is a automatic speech recognition model from Xenova. Use it when you need speech turned into text. It is set up for transformers.js. The card lists the license as apache-2.0.
openai/whisper-tiny.en with ONNX weights to be compatible with Transformers.js.
Downloads · 30 days
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.onnx1.9 GB · 100%
From the Hugging Face model README
openai/whisper-tiny.en with ONNX weights to be compatible with Transformers.js.
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
Example: Transcribe English.
import { pipeline } from '@huggingface/transformers';
// Create speech recognition pipeline
const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
// Transcribe audio from URL
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." }
Example: Transcribe English w/ timestamps.
import { pipeline } from '@huggingface/transformers';
// Create speech recognition pipeline
const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
// Transcribe audio from URL with timestamps
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const output = await transcriber(url, { return_timestamps: true });
// {
// text: " And so my fellow Americans ask not what your country can do for you, ask what you can do for your country."
// chunks: [
// { timestamp: [0, 8], text: " And so my fellow Americans ask not what your country can do for you" }
// { timestamp: [8, 11], text: " ask what you can do for your country." }
// ]
// }
Example: Transcribe English w/ word-level timestamps.
import { pipeline } from '@huggingface/transformers';
// Create speech recognition pipeline
const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
// Transcribe audio from URL with word-level timestamps
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const output = await transcriber(url, { return_timestamps: 'word' });
// {
// "text": " And so my fellow Americans ask not what your country can do for you ask what you can do for your country.",
// "chunks": [
// { "text": " And", "timestamp": [0, 0.78] },
// { "text": " so", "timestamp": [0.78, 1.06] },
// { "text": " my", "timestamp": [1.06, 1.46] },
// ...
// { "text": " for", "timestamp": [9.72, 9.92] },
// { "text": " your", "timestamp": [9.92, 10.22] },
// { "text": " country.", "timestamp": [10.22, 13.5] }
// ]
// }
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).