Instructions to use markusingvarsson/whisper-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use markusingvarsson/whisper-test with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('automatic-speech-recognition', 'markusingvarsson/whisper-test');
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Download README.md from markusingvarsson/whisper-test: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
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https://huggingface.co/markusingvarsson/whisper-test/resolve/main/README.md
- Command line
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hf download hf://markusingvarsson/whisper-test/README.md
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curl -L -o README.md https://huggingface.co/markusingvarsson/whisper-test/resolve/main/README.md
1.16 kB
metadata
library_name: transformers.js
tags:
- transformers.js
- onnx
- whisper
pipeline_tag: automatic-speech-recognition
Whisper Base ONNX
This is an ONNX conversion of OpenAI's whisper-base model, optimized for use with Transformers.js.
Model Details
- Model Type: Whisper (Encoder-Decoder)
- Task: Automatic Speech Recognition
- Format: ONNX (INT8 Quantized)
- Size: ~75MB (quantized from ~300MB)
Usage
import { pipeline } from '@huggingface/transformers';
const transcriber = await pipeline('automatic-speech-recognition', 'markusingvarsson/whisper-test');
const result = await transcriber('audio.wav');
console.log(result.text);
Conversion Details
This model was converted using a custom conversion pipeline that:
- Downloads the original HuggingFace model
- Exports to ONNX format with KV caching
- Applies INT8 quantization for smaller size
- Adds Whisper-specific alignment heads for timestamp support
The quantized models are approximately 4x smaller than the original while maintaining accuracy.