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Open-weight model · Speech recognition

whisper-tiny.en

by Joshua Xenova/whisper-tiny.en

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: Note: Having a separate repo for ONNX weights is intended to be a temporary solution…

Parameters
Context
Weights1.9 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads795.1k

Model Card

By Joshua, published under apache-2.0, revision 79fb389fc764.

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: 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).

Read Joshua's full model card

Whisper

openai/whisper-tiny.en 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 @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).

Configuration

Architecture
WhisperForConditionalGeneration
Layers
4
Vocabulary size
51,864
Model type
whisper

Identity and Version

Repository
Xenova/whisper-tiny.en
Publisher
Joshua
Task
Speech recognition
Modality
Audio
Library
transformers.js
Parameters
Not stated by the source
Languages
Not stated by the source
Revision
79fb389fc764e7c395bd330e9531d9d32ada7049
First published
2023-05-02
Last updated
2025-12-16

Files and Weights

45 files, 1.9 GB in total. The weights are 31 files totalling 1.9 GB in onnx.

Weights31 files · 1.9 GB
Configuration8 files · 66.3 KB
Tokenizer4 files · 3.6 MB
Documentation1 file · 3.2 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
onnx/decoder_model.onnxWeights118.4 MB 38f0b91131d1
onnx/decoder_model_bnb4.onnxWeights86.0 MB 95b70fcb440a
onnx/decoder_model_fp16.onnxWeights59.3 MB c048ad447e8f
onnx/decoder_model_int8.onnxWeights110.0 MB 3543fb176f09
onnx/decoder_model_merged.onnxWeights118.6 MB 950978b1dbcb
onnx/decoder_model_merged_bnb4.onnxWeights86.1 MB c18806ab89e8
onnx/decoder_model_merged_fp16.onnxWeights59.6 MB 2186c6567a2a
onnx/decoder_model_merged_int8.onnxWeights30.7 MB e28fbe180689
onnx/decoder_model_merged_q4.onnxWeights86.7 MB d9278519f295
onnx/decoder_model_merged_q4f16.onnxWeights46.0 MB 0321a7484af0
onnx/decoder_model_merged_quantized.onnxWeights30.7 MB dbb2e063b7fb
onnx/decoder_model_merged_uint8.onnxWeights30.7 MB d25cbda3719d
onnx/decoder_model_q4.onnxWeights86.5 MB 5145e4a1f874
onnx/decoder_model_q4f16.onnxWeights45.7 MB a825e1ef7a76
onnx/decoder_model_quantized.onnxWeights30.5 MB 62b1761a6c23
onnx/decoder_model_uint8.onnxWeights110.0 MB 31fe007826bd
onnx/decoder_with_past_model.onnxWeights113.7 MB 52b796cccc71
onnx/decoder_with_past_model_bnb4.onnxWeights85.3 MB dd7471775f5e
onnx/decoder_with_past_model_fp16.onnxWeights56.9 MB 03df0d6070df
onnx/decoder_with_past_model_int8.onnxWeights108.9 MB 9f7014b3faea
onnx/decoder_with_past_model_q4.onnxWeights85.8 MB 40a880819ea4
onnx/decoder_with_past_model_q4f16.onnxWeights45.1 MB ad8d96aeda21
onnx/decoder_with_past_model_quantized.onnxWeights29.2 MB 2c7390ddd38c
onnx/decoder_with_past_model_uint8.onnxWeights108.9 MB 50a73923d61f
onnx/encoder_model.onnxWeights32.9 MB a048dcf0cde9
onnx/encoder_model_bnb4.onnxWeights8.6 MB e38d6ab78841
onnx/encoder_model_fp16.onnxWeights16.5 MB 9642671531f3
onnx/encoder_model_q4.onnxWeights9.0 MB 72cda0fe855e
onnx/encoder_model_q4f16.onnxWeights6.3 MB b834b21e0f0b
onnx/encoder_model_quantized.onnxWeights10.1 MB 8cc3c6f8563d
onnx/encoder_model_uint8.onnxWeights10.1 MB 67316ca75ab1
added_tokens.jsonConfiguration2.1 KB
config.jsonConfiguration2.2 KB
generation_config.jsonConfiguration1.6 KB
normalizer.jsonConfiguration52.7 KB
preprocessor_config.jsonConfiguration339 B
quant_config.jsonConfiguration2.8 KB
quantize_config.jsonConfiguration2.8 KB
special_tokens_map.jsonConfiguration1.7 KB
README.mdDocumentation3.2 KB
.gitattributesRepository1.5 KB
merges.txtTokenizer456.3 KB
tokenizer.jsonTokenizer2.1 MB
tokenizer_config.jsonTokenizer835 B
vocab.jsonTokenizer999.2 KB

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
1.9 GB
Download from Joshua

Released by Joshua through its official repository on Hugging Face. Read the license.

Built From

  • Derived from openai/whisper-tiny.en
  • Quantized from openai/whisper-tiny.en

Memory Requirements

PrecisionWeights in memory
As published1.9 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About whisper-tiny.en

Can I use whisper-tiny.en commercially?

Yes. whisper-tiny.en is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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