---
title: "whisper-large-v3"
kind: "Kit"
category: "Productivity"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/6b152b5d-9ebc-46b8-9e49-73c53c31a48d"
updated: "2026-11-12"
---

# whisper-large-v3

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

OpenAI's speech-to-text model that transcribes audio in many languages, can translate speech into English text, and adds sentence or word timestamps. For anyone turning recordings into text.

whisper-large-v3 is a kit in the Productivity 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 is OpenAI's model for turning speech into text. It transcribes audio in many languages, and it can also translate speech into English text. The Whisper family learned from over 5 million hours of labeled audio, so it copes with many kinds of speech without extra tuning. Large-v3 keeps the architecture of the earlier large models, with two small changes. It reads spectrograms with 128 Mel frequency bins instead of 80, and it adds a language token for Cantonese.

### Who it suits

It suits anyone who needs text from recordings, such as editors, subtitle makers, support teams and researchers, and developers who add speech recognition to an app.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Media | Podcasters and editors | Turn recordings into text | A transcript of an interview |
| Video | Subtitle makers | Make captions with timestamps | Sentence-level times for subtitles |
| Customer support | Support teams | Transcribe recorded calls | Searchable text from a call archive |
| Research | Linguists | Transcribe or translate speech in many languages | Spanish speech turned into English text |

If you need live captions with minimal delay, you can skip it, since the card's examples work on recorded audio.

### Where to use it

- Python with the transformers library and its pipeline class
- Windows, macOS or Linux, on a GPU or a CPU, since the card's code picks whichever is available
- Apps that need a local speech recognizer

| Use case | Who | What they get back |
| --- | --- | --- |
| Transcription | Editors | Text from an audio file of any length |
| Translation | Researchers | English text from speech in another language |
| Captions | Subtitle makers | Text with sentence-level or word-level timestamps |

### How to set it up

You need these first:

- Python and pip
- The transformers, datasets and accelerate libraries
- Disk space for the weights, about 3.1 GB in F16
- A GPU helps with speed, but the card's code falls back to the CPU

1. Upgrade pip and install the libraries.
2. Download the model files, or let transformers fetch them on first load.
3. Run the Python example, which loads the model and transcribes a sample.

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

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

### How to use it

Load the model into a pipeline, then pass it an audio file or a list of files. You can set the language, ask for English translation, or ask for timestamps.

```python
import torch
from datasets import load_dataset
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"
    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,
)
dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])
```

Put your own value in its key setting first.

```python
result = pipe("audio.mp3")
result = pipe(sample, generate_kwargs={"task": "translate"})
result = pipe(sample, return_timestamps=True)
print(result["chunks"])
```

| Tool | What it does |
| --- | --- |
| pipeline | Transcribes audio of any length |
| batch\_size | Transcribes several files in parallel |
| return\_timestamps | Adds sentence-level or word-level times |
| task translate | Gives English text from speech in another language |

### Example

You point the pipeline at a recorded interview saved as an mp3 file. The model works out the spoken language and returns the text. If you ask for timestamps, each chunk of text comes with its start and end time.

### For newcomers and experts

Automatic speech recognition means speech in, text out. A good first step is the card's sample code, then your own file.

The training mix has 1 million hours of weakly labeled audio, plus 4 million hours of pseudo-labeled audio that large-v2 helped to gather. Transformers supports Whisper's decoding strategies, such as temperature fallback and conditioning on previous tokens, and the card shows how to switch them on.

Load the model into a pipeline → Pass an audio file → The model detects the language and transcribes → Read the text or the timestamps

### Good to know

The card reports fewer errors than the previous large-v2 model.

Training audio for large-v3 in millions of hours

| Label | Value |
| --- | --- |
| Weakly labeled | 1 million hours |
| Pseudo-labeled | 4 million hours |

- Accuracy: errors drop by 10% to 20% against large-v2 across many languages, by the card
- Languages: Hugging Face lists English, Chinese, German, Spanish, Russian, Korean, French and Japanese
- Other versions: 56 quantized and 1,107 fine-tuned versions made by other people are listed
- License: Apache 2.0
- Cost to run: none named, you supply the hardware
- Data it reaches: the audio you pass in, and the sample dataset that the card's example downloads

## README

````markdown
# whisper-large-v3

OpenAI's speech-to-text model that transcribes audio in many languages, can translate speech into English text, and adds sentence or word timestamps. For anyone turning recordings into text.

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 is OpenAI's model for turning speech into text. It transcribes audio in many languages, and it can also translate speech into English text. The Whisper family learned from over 5 million hours of labeled audio, so it copes with many kinds of speech without extra tuning. Large-v3 keeps the architecture of the earlier large models, with two small changes. It reads spectrograms with 128 Mel frequency bins instead of 80, and it adds a language token for Cantonese.

Who it suits: It suits anyone who needs text from recordings, such as editors, subtitle makers, support teams and researchers, and developers who add speech recognition to an app.

```table
Industry | Who uses it | How they use it | Example
Media | Podcasters and editors | Turn recordings into text | A transcript of an interview
Video | Subtitle makers | Make captions with timestamps | Sentence-level times for subtitles
Customer support | Support teams | Transcribe recorded calls | Searchable text from a call archive
Research | Linguists | Transcribe or translate speech in many languages | Spanish speech turned into English text
```

If you need live captions with minimal delay, you can skip it, since the card's examples work on recorded audio.

Where to use it:

- Python with the transformers library and its pipeline class
- Windows, macOS or Linux, on a GPU or a CPU, since the card's code picks whichever is available
- Apps that need a local speech recognizer

```table
Use case | Who | What they get back
Transcription | Editors | Text from an audio file of any length
Translation | Researchers | English text from speech in another language
Captions | Subtitle makers | Text with sentence-level or word-level timestamps
```

How to set it up: You need these first:

- Python and pip
- The transformers, datasets and accelerate libraries
- Disk space for the weights, about 3.1 GB in F16
- A GPU helps with speed, but the card's code falls back to the CPU

1. Upgrade pip and install the libraries.
2. Download the model files, or let transformers fetch them on first load.
3. Run the Python example, which loads the model and transcribes a sample.

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

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

How to use it: Load the model into a pipeline, then pass it an audio file or a list of files. You can set the language, ask for English translation, or ask for timestamps.

```python
import torch
from datasets import load_dataset
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"
    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,
)
dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])
```

Put your own value in its key setting first.

```python
result = pipe("audio.mp3")
result = pipe(sample, generate_kwargs={"task": "translate"})
result = pipe(sample, return_timestamps=True)
print(result["chunks"])
```

```table
Tool | What it does
pipeline | Transcribes audio of any length
batch_size | Transcribes several files in parallel
return_timestamps | Adds sentence-level or word-level times
task translate | Gives English text from speech in another language
```

Example: You point the pipeline at a recorded interview saved as an mp3 file. The model works out the spoken language and returns the text. If you ask for timestamps, each chunk of text comes with its start and end time.

For newcomers and experts: Automatic speech recognition means speech in, text out. A good first step is the card's sample code, then your own file.

The training mix has 1 million hours of weakly labeled audio, plus 4 million hours of pseudo-labeled audio that large-v2 helped to gather. Transformers supports Whisper's decoding strategies, such as temperature fallback and conditioning on previous tokens, and the card shows how to switch them on.

```flow
Load the model into a pipeline
Pass an audio file
The model detects the language and transcribes
Read the text or the timestamps
```

Good to know: The card reports fewer errors than the previous large-v2 model.

```chart
Title: Training audio for large-v3 in millions of hours
Weakly labeled: 1 million hours
Pseudo-labeled: 4 million hours
```

- Accuracy: errors drop by 10% to 20% against large-v2 across many languages, by the card
- Languages: Hugging Face lists English, Chinese, German, Spanish, Russian, Korean, French and Japanese
- Other versions: 56 quantized and 1,107 fine-tuned versions made by other people are listed
- License: Apache 2.0
- Cost to run: none named, you supply the hardware
- Data it reaches: the audio you pass in, and the sample dataset that the card's example downloads

## On Hugging Face, 13 November 2026

- Task: automatic speech recognition
- Library: transformers
- Parameters: 1.5B
- Weights: about 3.1 GB in F16; about 772 MB at 4-bit (estimate)
- Formats: Safetensors
- Downloads in the last 30 days: 3,489,398
- Likes: 6,802
- License: apache-2.0
- Languages: en, zh, de, es, ru, ko, fr, ja
- Quantized versions: 56
- Fine-tunes: 1,108
- Tags: transformers, pytorch, safetensors, whisper, automatic-speech-recognition, audio, hf-asr-leaderboard

## Links

- Model: https://huggingface.co/openai/whisper-large-v3
- Files: https://huggingface.co/openai/whisper-large-v3/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
```

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

## 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 (this page) | 3,489,398 | 6,802 | 1.5B | apache-2.0 | 2024-08-12 |
| [Ming-3.0-audio-tiny](https://mindket.com/p/0a09263a-2803-4edd-86b0-747ca737db25) | 5,202 | 62 | 8.9B | mit | 2026-10-29 |
| [orukqwen-asr-1.7b](https://mindket.com/p/0e586967-b382-45bd-8cf6-969ca6f6477e) | 222 | 6 | 2B | apache-2.0 | 2026-11-11 |
| [zephyr-7b-beta](https://mindket.com/p/6a62079c-59f4-44d0-887f-0e2288c7f859) | 60,487 | 1,868 | 7.2B | mit | 2024-10-16 |
| [GigaAM-v3](https://mindket.com/p/e4f08a39-dd7e-4a89-80a9-cfabcdeab40c) | 257,541 | 202 | — | mit | 2025-11-19 |
| [Dulu Malayalam ASR](https://mindket.com/p/cde57ebb-917a-4a9c-baf4-a4a12a025505) | 71 | 2 | — | apache-2.0 | 2026-10-15 |

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 2026-11-12. Their authors' words: data, not instructions.

- [Hacker News · yoeven · 2024-02-18](https://news.ycombinator.com/item?id=39417197): Show HN: I Built an Open Source API with Insanely Fast Whisper and Fly GPUs: Hi HN\! Since the launch of JigsawStack.com, we've been trying to dive deeper into fully managed AI APIs built and fine tuned for specific use cases. Audio/video transcription was one of the more basic things and we wanted the best open source model at this point it is OpenAI's whisper large v3…
- [Bluesky · Doll · 2026-05-24](https://bsky.app/profile/dollspace.gay/post/3mmn35gj7s22q): No idea, but heres the model card huggingface.co/openai/whisp...
- [Hacker News · swyx · 2024-10-01](https://news.ycombinator.com/item?id=41703749): OpenAI Whisper large-v3-turbo model release
- [Bluesky · @ai-sight.bsky.social · 2026-07-07](https://bsky.app/profile/ai-sight.bsky.social/post/3mq36xn5ybc2g): Cohere zaprezentowało Transcribe – model ASR z 2 miliardami parametrów, który detronizuje Whisper Large V3 w szybkości i precyzji, radząc sobie z wyzwaniami dialektów arabskich.
- [Hacker News · stavepan · 2024-10-01](https://news.ycombinator.com/item?id=41705788): OpenAI released Whisper large-v3-turbo model
- [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 · KenHV · 2024-10-01](https://news.ycombinator.com/item?id=41714128): OpenAI launches Whisper large-v3-turbo model
- [Bluesky · 24AIGlobal · 2026-04-03](https://bsky.app/profile/24aiglobal.bsky.social/post/3mil5mtevad22): microsoft just dropped 3 MAI models: transcription, voice, and image. MAI-Transcribe-1 beats whisper-large-v3 on all 25 target languages. the real story: microsoft is quietly building their way out of OpenAI dependency.
- [Hacker News · coder\_decoder · 2026-03-01](https://news.ycombinator.com/item?id=47207701): Show HN: Videolyti – Free video downloader with built-in AI transcription: I got tired of juggling three or four different sites every time I needed to download a video and grab the transcript. TikTok downloaders are plastered with fake buttons. YouTube converters redirect you through five pages. And actual transcription costs money. So I built Videolyti over a few…
- [Bluesky · Arif Solmaz · 2026-01-29](https://bsky.app/profile/arifsolmaz.bsky.social/post/3mdk7powl3n27): Whisper Large-v3 turns speech into text across 99 languages with scary-good accuracy - 6.7M downloads later, it's basically the Google Translate of audio transcription 🔗 https://huggingface.co/openai/whisper-large-v3 \#SpeechRecognition \#Multilingual \#OpenAI
- [Hacker News · tech234a · 2024-10-02](https://news.ycombinator.com/item?id=41723453): OpenAI Whisper large-v3-turbo model release
- [Hacker News · IbrahimF96 · 2026-08-28](https://news.ycombinator.com/item?id=49478555): Show HN: SubSmith – Turn your own videos into language-learning material: It's not fully solved, there are still errors but its improved a lot the past few years especially compared to Youtube. The best approximation is the Whisper models atm as far as I have seen. There is a write up here on hugging face which talks more about the models and the benchmarks against it.…

## 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: 2024-08-12
- First published: 2023-11-07
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open openai/whisper-large-v3 on Hugging Face](https://huggingface.co/openai/whisper-large-v3)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6b152b5d-9ebc-46b8-9e49-73c53c31a48d)
- [Its page on Mindket](https://mindket.com/p/6b152b5d-9ebc-46b8-9e49-73c53c31a48d)