---
title: "OWSM v4"
kind: "Kit"
category: "Research"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/d6ca1d07-ce5a-42c1-bc50-110791787287"
updated: "2026-11-12"
---

# OWSM v4

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

Transcribes speech, translates it between languages, identifies the spoken language and decodes long recordings with the OWSM v4 model, with an optional text prompt. It suits speech researchers and anyone testing multilingual audio.

OWSM v4 is a kit in the Research 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 espnet

## About

### What it is

OWSM v4 is a speech model demo from the ESPnet project. It takes 151 languages in and gives 25 translation targets out, and you can steer it with a text prompt. It can transcribe speech, translate it from any language to any other, name the spoken language and decode long recordings. The Space runs the owsm\_v4\_medium\_1B model on ZeroGPU hardware.

### Who it suits

It suits speech researchers, developers and anyone with multilingual recordings who wants to test an open speech model.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Speech research | Researchers | Compare an encoder-decoder model with the CTC demo on the same audio | A researcher runs one clip through both demos. |
| Media and archives | Archivists | Transcribe recordings in many languages | An archivist uploads an interview and reads the transcript. |
| Localization | Translators | Get a first draft translation of spoken audio | A translator picks a target language for a short recording. |
| Software | Developers | Read how a demo loads a model and asks for GPU time | A developer opens app.py to see the device choice. |

If your recordings run longer than two minutes, the hosted demo will not take them, but a local copy can.

### Where to use it

- A web browser, through the Space's page on Hugging Face
- A local copy on Windows, macOS or Linux, using Python 3.12

| Use case | Who | What they get back |
| --- | --- | --- |
| Speech recognition | Anyone | A transcript of the audio |
| Speech translation | Translators | The speech rendered in one of the 25 target languages |
| Language identification | Researchers | The language the speaker used |
| Long recordings | Developers | Decoding of audio longer than the hosted limit, run locally |

### How to set it up

- Python 3.12, since the project needs 3.12 or 3.13
- Python packages from requirements.txt
- A GPU for speed, though the DEVICE setting can force CPU

1. Install the huggingface\_hub package.
2. Download the Space into a local folder named owsm-v4.
3. Move into that folder.
4. Install gradio at version 6.27.0.
5. Install the packages listed in requirements.txt.
6. Start the app.

```sh
pip install -U huggingface_hub
hf download espnet/owsm-v4 --repo-type space --local-dir owsm-v4
cd owsm-v4
pip install gradio==6.27.0
pip install -r requirements.txt
python app.py
```

### How to use it

Open the Space's page, or start the app locally with the command below. Upload or record audio, pick a task and, if you like, add a text prompt. The hosted demo takes up to two minutes of audio.

```sh
python app.py
```

| Tool | What it does |
| --- | --- |
| Speech recognition | Turns speech into text |
| Any-to-any translation | Renders speech in another language |
| Language identification | Detects which language is spoken |
| Long-form decoding | Handles recordings longer than one window |
| Text prompt | Primes the decoder, which this model supports |

The repository also holds a second demo, OWSM-CTC v4, so you can compare the two on the same audio.

| Aspect | This demo | The CTC demo |
| --- | --- | --- |
| Model | owsm\_v4\_medium\_1B, encoder-decoder | owsm\_ctc\_v4\_1B, encoder-only |
| Decoding | Beam search | One encoder pass per 30 second window |
| Text prompt | Supported | Not supported |

### Example

You upload a recording of about a minute, choose speech recognition and leave the prompt empty. The demo returns the transcript. You then switch the task to translation and pick a target language.

### For newcomers and experts

If you are new, upload a short clip and run speech recognition first. That shows what the model hears before you try translation or prompts.

If you know the field, the demo page comes from the ESPnet demo module, which reads the language menu, the translation targets and the prompt option off the checkpoint. This model has a decoder that a prompt can steer, which is why the option shows up here. The method is described in an Interspeech 2025 paper.

Upload or record audio → Pick a task and add a prompt → The model runs on a ZeroGPU slice → Text or translation comes back

Language coverage

| Label | Value |
| --- | --- |
| Languages in | 151 languages |
| Translation targets out | 25 languages |

### Good to know

- The hosted demo accepts up to two minutes of audio, though the model has no such limit
- Setting DEVICE=cpu forces a CPU run, even on a machine with a GPU
- License: apache-2
- Cost to run: ZeroGPU hardware on Hugging Face for the hosted demo, and none named for a local run
- Data it reaches: the audio you give it
- Not for: recordings over two minutes on the hosted demo

## README

````markdown
# OWSM v4

Transcribes speech, translates it between languages, identifies the spoken language and decodes long recordings with the OWSM v4 model, with an optional text prompt. It suits speech researchers and anyone testing multilingual audio.

Made by espnet on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

## About

What it is: OWSM v4 is a speech model demo from the ESPnet project. It takes 151 languages in and gives 25 translation targets out, and you can steer it with a text prompt. It can transcribe speech, translate it from any language to any other, name the spoken language and decode long recordings. The Space runs the owsm_v4_medium_1B model on ZeroGPU hardware.

Who it suits: It suits speech researchers, developers and anyone with multilingual recordings who wants to test an open speech model.

```table
Industry | Who uses it | How they use it | Example
Speech research | Researchers | Compare an encoder-decoder model with the CTC demo on the same audio | A researcher runs one clip through both demos.
Media and archives | Archivists | Transcribe recordings in many languages | An archivist uploads an interview and reads the transcript.
Localization | Translators | Get a first draft translation of spoken audio | A translator picks a target language for a short recording.
Software | Developers | Read how a demo loads a model and asks for GPU time | A developer opens app.py to see the device choice.
```

If your recordings run longer than two minutes, the hosted demo will not take them, but a local copy can.

Where to use it:

- A web browser, through the Space's page on Hugging Face
- A local copy on Windows, macOS or Linux, using Python 3.12

```table
Use case | Who | What they get back
Speech recognition | Anyone | A transcript of the audio
Speech translation | Translators | The speech rendered in one of the 25 target languages
Language identification | Researchers | The language the speaker used
Long recordings | Developers | Decoding of audio longer than the hosted limit, run locally
```

How to set it up:

- Python 3.12, since the project needs 3.12 or 3.13
- Python packages from requirements.txt
- A GPU for speed, though the DEVICE setting can force CPU

1. Install the huggingface_hub package.
2. Download the Space into a local folder named owsm-v4.
3. Move into that folder.
4. Install gradio at version 6.27.0.
5. Install the packages listed in requirements.txt.
6. Start the app.

```sh
pip install -U huggingface_hub
hf download espnet/owsm-v4 --repo-type space --local-dir owsm-v4
cd owsm-v4
pip install gradio==6.27.0
pip install -r requirements.txt
python app.py
```

How to use it: Open the Space's page, or start the app locally with the command below. Upload or record audio, pick a task and, if you like, add a text prompt. The hosted demo takes up to two minutes of audio.

```sh
python app.py
```

```table
Tool | What it does
Speech recognition | Turns speech into text
Any-to-any translation | Renders speech in another language
Language identification | Detects which language is spoken
Long-form decoding | Handles recordings longer than one window
Text prompt | Primes the decoder, which this model supports
```

The repository also holds a second demo, OWSM-CTC v4, so you can compare the two on the same audio.

```table
Aspect | This demo | The CTC demo
Model | owsm_v4_medium_1B, encoder-decoder | owsm_ctc_v4_1B, encoder-only
Decoding | Beam search | One encoder pass per 30 second window
Text prompt | Supported | Not supported
```

Example: You upload a recording of about a minute, choose speech recognition and leave the prompt empty. The demo returns the transcript. You then switch the task to translation and pick a target language.

For newcomers and experts: If you are new, upload a short clip and run speech recognition first. That shows what the model hears before you try translation or prompts.

If you know the field, the demo page comes from the ESPnet demo module, which reads the language menu, the translation targets and the prompt option off the checkpoint. This model has a decoder that a prompt can steer, which is why the option shows up here. The method is described in an Interspeech 2025 paper.

```flow
Upload or record audio
Pick a task and add a prompt
The model runs on a ZeroGPU slice
Text or translation comes back
```

```chart
Title: Language coverage
Languages in: 151 languages
Translation targets out: 25 languages
```

Good to know:

- The hosted demo accepts up to two minutes of audio, though the model has no such limit
- Setting DEVICE=cpu forces a CPU run, even on a machine with a GPU
- License: apache-2
- Cost to run: ZeroGPU hardware on Hugging Face for the hosted demo, and none named for a local run
- Data it reaches: the audio you give it
- Not for: recordings over two minutes on the hosted demo

## On Hugging Face, 13 November 2026

- Likes: 1
- Built with: gradio
- License: apache-2.0

## Links

- Space: https://huggingface.co/spaces/espnet/owsm-v4
- Files: https://huggingface.co/spaces/espnet/owsm-v4/tree/main

## Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.
````

## What is in it

1 files, 5 KB, downloaded as a zip.

```text
README.md  5 KB
```

## Source

[espnet/owsm-v4 on Hugging Face](https://huggingface.co/spaces/espnet/owsm-v4)

## Compare with similar

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

| Product | Likes | Built with | License | Last change |
| --- | --- | --- | --- | --- |
| OWSM v4 (this page) | 1 | gradio | apache-2.0 | 2026-10-10 |
| [OWSM v4 by espnet](https://mindket.com/p/1836c5fe-f038-4b8e-b5a5-07d275bfae11) | 12 | gradio | apache-2.0 | 2026-10-10 |
| [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 |
| [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 |
| [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 |
| [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 |

Numbers as Mindket last read them, on 2026-11-12.

## 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: 2026-10-10
- First published: 2026-09-18
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open espnet/owsm-v4 on Hugging Face](https://huggingface.co/spaces/espnet/owsm-v4)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d6ca1d07-ce5a-42c1-bc50-110791787287)
- [Its page on Mindket](https://mindket.com/p/d6ca1d07-ce5a-42c1-bc50-110791787287)