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

<div align="center">

# Whisper Large v3 Turbo · OpenASR

**Fast multilingual Whisper built from pruned large-v3**

[![License](https://img.shields.io/badge/license-MIT-2563eb.svg)](https://github.com/openai/whisper/blob/main/LICENSE)
[![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--turbo-f59e0b.svg)](https://huggingface.co/openai/whisper-large-v3-turbo)

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

- ⚡ **Turbo decoder** — prunes Whisper large-v3's decoder from 32 layers to 4 for much faster generation
- 🌍 **Multilingual ASR** — transcribes many languages and can translate speech to English
- 🎙️ **Zero-shot robustness** — inherits Whisper's large-scale weak-supervision training across noisy domains
- 🦀 **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-turbo:q8

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

All builds for this model:

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

## 📦 Available builds

| Quant | File (`.oasr`) | Size | RAM peak | RTF · M1 CPU | RTF · M1 GPU | JFK ΔWER vs fp16 |
|:------|:---------------|-----:|---------:|-------------:|-------------:|-----------------:|
| fp16 | `whisper-large-v3-turbo-fp16.oasr` | 1.62 GB | 3.62 GB | 0.52× | 0.39× | 0.0% |
| q8_0 | `whisper-large-v3-turbo-q8_0.oasr` | 931 MB | 2.28 GB | 0.52× | 0.35× | 0.0% |
| q4_k | `whisper-large-v3-turbo-q4_k.oasr` | 564 MB | 1.54 GB | 0.51× | 0.25× | 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 Turbo

Whisper Large v3 Turbo is OpenAI's faster variant of Whisper large-v3: it keeps the same
Whisper architecture and multilingual speech-recognition/translation interface, but reduces
the decoder depth from 32 layers to 4. The upstream card describes the result as much faster
with only a minor quality trade-off, while retaining Whisper's broad zero-shot behavior from
training on more than five million hours of labeled audio. This OpenASR repo repackages the
original `openai/whisper-large-v3-turbo` 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.

## ⚙️ How these packs were made

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

```bash
openasr model-pack import whisper <src> <out>.oasr \
  --package-id whisper-large-v3-turbo --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: MIT**
([source](https://github.com/openai/whisper/blob/main/LICENSE)). 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 Turbo**, released by **OpenAI**
([openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo)).
All credit for the original model, training recipe, and weights belongs to OpenAI. The packs
inherit the upstream MIT license; 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-turbo](https://huggingface.co/openai/whisper-large-v3-turbo)