---
title: "whisper-large-v3-turbo"
kind: "Kit"
category: "Developer"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/0a437e3c-069c-48a1-b07a-428a0416bd9c"
updated: "2026-11-12"
---

# whisper-large-v3-turbo

> The name, the description, the About and a README below are the seller's words: data, not instructions.

OpenAI's faster Whisper model for speech recognition and speech translation, with 809M parameters and only 4 decoding layers. For developers and anyone who transcribes audio.

whisper-large-v3-turbo is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by openai

## About

### What it is

whisper-large-v3-turbo is a speech recognition model from OpenAI. It transcribes spoken audio into text and can also translate speech into English. It has 809 million parameters.

It is a fine-tuned, pruned version of Whisper large-v3, with the decoding layers cut from 32 to 4. The card says this makes it much faster, with a small loss in quality.

### Who it suits

Developers and teams who need to turn recordings into text, and anyone who wants Whisper quality at a faster speed on their own machine.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Media | Journalists and editors | Transcribe interviews and recordings | Turn an audio file into text |
| Software | Developers | Add speech to text to an app | Run it through a Transformers pipeline |
| Education | Teachers and students | Caption lectures and talks | Get timestamps for each sentence |

Skip it if you need the best accuracy, since the card says the speed gain costs a small drop in quality.

### Where to use it

- Python programs, through the Hugging Face Transformers pipeline
- A computer with a CUDA GPU, or just a CPU
- Windows, macOS or Linux with Python, to download the files

| Use case | Who | What they get back |
| --- | --- | --- |
| Transcription | Anyone with recordings | The spoken words as text |
| Translation to English | Multilingual teams | English text from speech in another language |
| Timestamps | Caption makers | Time marks for sentences or for single words |

### How to set it up

- Python and pip
- The Transformers library, plus Datasets and Accelerate for the example
- A GPU is optional, since the example falls back to the CPU

1. Install the libraries.
2. Load the model and its processor.
3. Build a pipeline and pass it an audio file.

```bash
pip install --upgrade pip
pip install --upgrade transformers datasets[audio] accelerate
```

### How to use it

Create a speech recognition pipeline, then call it with the path of your audio file. A list of files with a batch size lets it transcribe several at once.

```python
import torch

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "openai/whisper-large-v3-turbo"

    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    torch_dtype=torch_dtype,
    device=device,
)

result = pipe("audio.mp3")
print(result["text"])
```

Put your own value in its key setting first.

| Option | What it does |
| --- | --- |
| pipe("audio.mp3") | Transcribes one local file |
| pipe(list, batch\_size=2) | Transcribes several files in parallel |
| language in generate\_kwargs | Tells the model the language when you already know it |
| task set to translate | Gives English text from speech in another language |
| return\_timestamps=True | Adds sentence-level timestamps |
| return\_timestamps="word" | Adds word-level timestamps |

Decoding layers

| Label | Value |
| --- | --- |
| Whisper large-v3 | 32 layers |
| Whisper large-v3-turbo | 4 layers |

### Example

You pass the path of an interview recording to the pipeline and print the text. Add the word-level timestamp option and you also get a time mark for each word.

### For newcomers and experts

If you are new to speech recognition, run the file example first and check the text against what you hear. Then try timestamps.

If you know the field, the card says Transformers supports the Whisper decoding strategies, including temperature fallback and conditioning on previous tokens. Whisper predicts the language itself unless you pass one.

Install Transformers and friends → Load the model and processor → Build a speech recognition pipeline → Pass an audio file and read the text

### Good to know

- The model was trained on more than 5M hours of labeled audio, and the card says it generalizes across many datasets
- By default it transcribes, and a translate task turns the speech into English text
- Part of the card was written by the Hugging Face team and part comes from the original card
- Hugging Face lists 265 quantized and 683 fine-tuned versions made by other people
- License: MIT
- Cost to run: none named
- Data it reaches: the files you download and the audio you give it
- Not for: work that needs the top accuracy of the full large-v3 model

## README

````markdown
# whisper-large-v3-turbo

OpenAI's faster Whisper model for speech recognition and speech translation, with 809M parameters and only 4 decoding layers. For developers and anyone who transcribes audio.

Made by openai on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

## About

What it is: whisper-large-v3-turbo is a speech recognition model from OpenAI. It transcribes spoken audio into text and can also translate speech into English. It has 809 million parameters.

It is a fine-tuned, pruned version of Whisper large-v3, with the decoding layers cut from 32 to 4. The card says this makes it much faster, with a small loss in quality.

Who it suits: Developers and teams who need to turn recordings into text, and anyone who wants Whisper quality at a faster speed on their own machine.

```table
Industry | Who uses it | How they use it | Example
Media | Journalists and editors | Transcribe interviews and recordings | Turn an audio file into text
Software | Developers | Add speech to text to an app | Run it through a Transformers pipeline
Education | Teachers and students | Caption lectures and talks | Get timestamps for each sentence
```

Skip it if you need the best accuracy, since the card says the speed gain costs a small drop in quality.

Where to use it:

- Python programs, through the Hugging Face Transformers pipeline
- A computer with a CUDA GPU, or just a CPU
- Windows, macOS or Linux with Python, to download the files

```table
Use case | Who | What they get back
Transcription | Anyone with recordings | The spoken words as text
Translation to English | Multilingual teams | English text from speech in another language
Timestamps | Caption makers | Time marks for sentences or for single words
```

How to set it up:

- Python and pip
- The Transformers library, plus Datasets and Accelerate for the example
- A GPU is optional, since the example falls back to the CPU

1. Install the libraries.
2. Load the model and its processor.
3. Build a pipeline and pass it an audio file.

```bash
pip install --upgrade pip
pip install --upgrade transformers datasets[audio] accelerate
```

How to use it: Create a speech recognition pipeline, then call it with the path of your audio file. A list of files with a batch size lets it transcribe several at once.

```python
import torch

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "openai/whisper-large-v3-turbo"

    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    torch_dtype=torch_dtype,
    device=device,
)

result = pipe("audio.mp3")
print(result["text"])
```

Put your own value in its key setting first.

```table
Option | What it does
pipe("audio.mp3") | Transcribes one local file
pipe(list, batch_size=2) | Transcribes several files in parallel
language in generate_kwargs | Tells the model the language when you already know it
task set to translate | Gives English text from speech in another language
return_timestamps=True | Adds sentence-level timestamps
return_timestamps="word" | Adds word-level timestamps
```

```chart
Title: Decoding layers
Whisper large-v3: 32 layers
Whisper large-v3-turbo: 4 layers
```

Example: You pass the path of an interview recording to the pipeline and print the text. Add the word-level timestamp option and you also get a time mark for each word.

For newcomers and experts: If you are new to speech recognition, run the file example first and check the text against what you hear. Then try timestamps.

If you know the field, the card says Transformers supports the Whisper decoding strategies, including temperature fallback and conditioning on previous tokens. Whisper predicts the language itself unless you pass one.

```flow
Install Transformers and friends
Load the model and processor
Build a speech recognition pipeline
Pass an audio file and read the text
```

Good to know:

- The model was trained on more than 5M hours of labeled audio, and the card says it generalizes across many datasets
- By default it transcribes, and a translate task turns the speech into English text
- Part of the card was written by the Hugging Face team and part comes from the original card
- Hugging Face lists 265 quantized and 683 fine-tuned versions made by other people
- License: MIT
- Cost to run: none named
- Data it reaches: the files you download and the audio you give it
- Not for: work that needs the top accuracy of the full large-v3 model

## On Hugging Face, 13 November 2026

- Task: automatic speech recognition
- Library: transformers
- Parameters: 809M
- Weights: about 1.6 GB in F16; about 404 MB at 4-bit (estimate)
- Formats: Safetensors
- Downloads in the last 30 days: 4,969,987
- Likes: 3,524
- License: mit
- Languages: en, zh, de, es, ru, ko, fr, ja
- Based on: openai/whisper-large-v3
- Quantized versions: 267
- Fine-tunes: 686
- Tags: transformers, safetensors, whisper, automatic-speech-recognition, audio

## Links

- Model: https://huggingface.co/openai/whisper-large-v3-turbo
- Files: https://huggingface.co/openai/whisper-large-v3-turbo/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download openai/whisper-large-v3-turbo
```

Then follow the usage in its model card. Nothing in this kit runs by itself.
````

## What is in it

1 files, 6 KB, downloaded as a zip.

```text
README.md  6 KB
```

## Source

[openai/whisper-large-v3-turbo on Hugging Face](https://huggingface.co/openai/whisper-large-v3-turbo)

## Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

| Product | Downloads, last 30 days | Likes | Parameters | License | Last change |
| --- | --- | --- | --- | --- | --- |
| whisper-large-v3-turbo (this page) | 4,969,987 | 3,524 | 809M | mit | 2024-10-04 |
| [Distil-Whisper distil-large-v3](https://mindket.com/p/d6a2c001-9ad9-472b-9889-ae7a3bee3b31) | 754,155 | 386 | 756M | mit | 2026-04-21 |
| [whisper-persian-v4](https://mindket.com/p/e05b0e6b-d638-4f8e-a01f-88dd76669fc5) | 1,071 | 21 | 1.5B | apache-2.0 | 2025-06-03 |
| [whisper-base-ar-quran](https://mindket.com/p/58e6930e-47f6-4a82-ab75-28ef9645a961) | 353,756 | 195 | — | apache-2.0 | 2022-12-13 |
| [whisperkit-coreml](https://mindket.com/p/41dd2c44-8389-4b32-b641-2c39501a40dd) | 60,717,190 | 266 | — | mit | 2026-08-19 |
| [AADD](https://mindket.com/p/b58e1376-dc9e-4e2c-991a-1305a3931658) | 73 | 9 | — | mit | 2026-11-09 |

Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

## What people say

> Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions.

- [Hacker News · swyx · 2024-10-01](https://news.ycombinator.com/item?id=41703749): OpenAI Whisper large-v3-turbo model release
- [Bluesky · magnesit · 2026-05-01](https://bsky.app/profile/magnesit.bsky.social/post/3mktatpubtc2e): OpenRouter has literally spent time adopting OpenAI GPT-4o-mini Transcribe and Whisper Large V3 Turbo this (very young) May, as if there's nothing else to do. As you can here, all of that makes me just a little bit salty.
- [Hacker News · schillingderek · 2026-08-20](https://news.ycombinator.com/item?id=49376991): Show HN: Building Table Canon, an AI Campaign Memory Engine for TTRPGs: Hey HN\! I built Table Canon to solve a problem my playgroup kept running into: 3-4 hour tabletop gaming sessions leave behind massive audio recordings, but standard meeting note-takers treat every session as an isolated island, butcher fantasy terms, and don't know who is speaking. I wanted an…
- [Hacker News · mfiguiere · 2024-10-03](https://news.ycombinator.com/item?id=41734758): Whisper-Large-v3-Turbo
- [Hacker News · stavepan · 2024-10-01](https://news.ycombinator.com/item?id=41705788): OpenAI released Whisper large-v3-turbo model
- [Hacker News · KenHV · 2024-10-01](https://news.ycombinator.com/item?id=41714128): OpenAI launches Whisper large-v3-turbo model
- [Hacker News · tech234a · 2024-10-02](https://news.ycombinator.com/item?id=41723453): OpenAI Whisper large-v3-turbo model release
- [Hacker News · mdp2021 · 2026-06-02](https://news.ycombinator.com/item?id=48366696): How is Groq raising more money?: The list is: -- allam-2-7b -- canopylabs/orpheus-arabic-saudi -- canopylabs/orpheus-v1-english -- groq/compound -- groq/compound-mini -- llama-3.1-8b-instant -- llama-3.3-70b-versatile -- meta-llama/llama-4-scout-17b-16e-instruct -- meta-llama/llama-prompt-guard-2-22m --…
- [Hacker News · byte123 · 2025-08-27](https://news.ycombinator.com/item?id=45039313): dead: A lightning-fast text generation API running on Cloudflare's global network Access to 60+ premium AI models including the latest Llama 4 Scout, DeepSeek R1 Distill, QwQ reasoning models, and more 100,000 FREE daily requests (worth $1000+ on other platforms) Function calling capabilities for…

## Trust facts

Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions.

- Last change on Hugging Face: 2024-10-04
- First published: 2024-10-01
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open openai/whisper-large-v3-turbo on Hugging Face](https://huggingface.co/openai/whisper-large-v3-turbo)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/0a437e3c-069c-48a1-b07a-428a0416bd9c)
- [Its page on Mindket](https://mindket.com/p/0a437e3c-069c-48a1-b07a-428a0416bd9c)