---
license: apache-2.0
base_model: openai/whisper-large-v3
pipeline_tag: automatic-speech-recognition
library_name: openasr
tags:
  - automatic-speech-recognition
  - speech-to-text
  - openasr
  - oasr
  - whisper-large-v3
---

<div align="center">

# Whisper Large v3 · OpenASR

**OpenAI's most accurate Whisper, the v3 large checkpoint**

[![License](https://img.shields.io/badge/license-Apache--2.0-2563eb.svg)](https://huggingface.co/openai/whisper-large-v3/blob/main/README.md)
[![Format](https://img.shields.io/badge/format-.oasr-7c3aed.svg)](https://github.com/QuintinShaw/openasr)
[![Runtime](https://img.shields.io/badge/runtime-OpenASR-111827.svg)](https://openasr.org)
[![Base model](https://img.shields.io/badge/base-whisper--large--v3-f59e0b.svg)](https://huggingface.co/openai/whisper-large-v3)

Native speech-to-text in the **[OpenASR](https://github.com/QuintinShaw/openasr)** runtime —
engineered for peak performance on CPU & GPU, **no Python at inference time**.

</div>

---

## ✨ Highlights

- 🎧 **Multilingual ASR** — transcribes a wide range of languages and can translate speech to English
- 🏆 **1.55B parameters** — the full-size Whisper, OpenAI's highest-accuracy checkpoint
- 🔁 **v3 improvements** — trained on a larger, more diverse corpus with 128 mel bins for better robustness
- 🦀 **Native in OpenASR** — `.oasr` packs run with no Python at inference, engineered for peak performance on CPU & GPU

## 🚀 Quickstart

```bash
# 1. Install the OpenASR CLI  ·  https://openasr.org
# 2. Pull a build (pick a quant — see the table below)
openasr pull whisper-large-v3:q8

# 3. Transcribe
openasr transcribe audio.wav --model whisper-large-v3
```

All builds for this model:

```bash
openasr pull whisper-large-v3:fp16
openasr pull whisper-large-v3:q8
openasr pull whisper-large-v3:q4
```

## 📦 Available builds

| Quant | File (`.oasr`) | Size | RAM peak | RTF · M1 CPU | RTF · M1 GPU | JFK ΔWER vs fp16 |
|:------|:---------------|-----:|---------:|-------------:|-------------:|-----------------:|
| fp16 | `whisper-large-v3-fp16.oasr` | 3.09 GB | 4.70 GB | 1.17× | 1.13× | 0.0% |
| q8_0 | `whisper-large-v3-q8_0.oasr` | 1.71 GB | 4.05 GB | 0.65× | 0.46× | 0.0% |
| q4_k | `whisper-large-v3-q4_k.oasr` | 978 MB | 2.46 GB | 0.61× | 0.49× | 0.0% |

<sub>RTF = real-time factor on the fixed 11s JFK clip (**lower is faster**); RAM peak measured per pack
in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's
fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy.
**q8_0** is the recommended default — near-reference quality at a fraction of the
footprint.</sub>

## 🧠 About Whisper Large v3

Whisper Large v3 is OpenAI's 1.55B-parameter multilingual Whisper checkpoint, the most accurate
member of the family. It uses the standard Whisper encoder-decoder architecture for automatic
speech recognition and speech translation; v3 was trained on a larger and more diverse labelled
corpus and uses 128 mel-frequency bins, improving robustness across languages and conditions
over earlier large checkpoints. This OpenASR repo repackages the original
`openai/whisper-large-v3` weights as `.oasr` packs that run natively in the OpenASR runtime with
no Python at inference time. For most users the q8_0 build is the recommended default; q4_k is
for tighter memory budgets and fp16 is for verification or maximum fidelity. For a faster
large-grade option, see the distilled `whisper-large-v3-turbo`.

## ⚙️ How these packs were made

Converted from [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) with the OpenASR importer:

```bash
openasr model-pack import whisper <src> <out>.oasr \
  --package-id whisper-large-v3 --quantization {fp16,q8-0,q4-k}
```

The `.oasr` container is GGUF-backed; packs use zero-copy mmap weight binding and graph
buffer reuse to keep peak memory low.

## ⚖️ License

These packs **inherit the upstream model's license: Apache-2.0**
([source](https://huggingface.co/openai/whisper-large-v3/blob/main/README.md)). OpenASR packaging retains the upstream copyright and
NOTICE; the only modifications are format conversion and quantization.

## 🙏 Acknowledgements

This pack is a redistribution of **Whisper Large v3**, released by **OpenAI**
([openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3)).
All credit for the original model, training recipe, and weights belongs to OpenAI. The
upstream Hugging Face model card declares Apache-2.0 licensing; OpenASR only converts the
weights into `.oasr` packages and adds quantized builds for local runtime use.

## 🔗 Links

- 🦀 **OpenASR** — <https://github.com/QuintinShaw/openasr>
- 🌐 **Website** — <https://openasr.org>
- 🤗 **Upstream model** — [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3)