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---
title: Whisper V3 Playground
description: Translate audio snippets into text on a Streamlit playground.
version: EN
---
## Try out this model on [VESSL Hub](https://vessl.ai/hub).
This example runs a general-purpose speech recognition model, [Whisper V3](https://github.com/openai/whisper). It is trained on a 680k hours of diverse labelled audio. Whisper is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. It can generalize to many domains without additional fine-tuning.
<img
className="rounded-md"
src="/images/whisper-results.png"
/>
## Running the model
You can run the model with our quick command.
```sh
vessl run create -f whisper.yaml
```
If you open log pages, you can see the result of inference for first 5 data in [Librispeech_asr dataset](https://www.openslr.org/12).
Here's a rundown of the `whisper.yaml` file.
```yaml
name: whisper-v3
description: A template Run for inference of whisper v3 on librispeech_asr test set
resources:
cluster: vessl-gcp-oregon
preset: v1.l4-1.mem-42
image: quay.io/vessl-ai/hub:torch2.1.0-cuda12.2-202312070053
import:
/model/: hf://huggingface.co/VESSL/Whisper-large-v3
/dataset/: hf://huggingface.co/datasets/VESSL/librispeech_asr_clean_test
/code/:
git:
url: https://github.com/vessl-ai/hub-model
ref: main
run:
- command: |-
pip install -r requirements.txt
python inference.py
workdir: /code/whisper-v3
```
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