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---
language:
- en
tags:
- audio
- automatic-speech-recognition
license: mit
---
# Distil-Whisper: distil-large-v3 for OpenAI Whisper
This repository contains the model weights for [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3)
converted to [OpenAI Whisper](https://github.com/openai/whisper) format.
Compared to previous Distil-Whisper releases, distil-large-v3 is specifically designed to be compatible
with the OpenAI Whisper long-form transcription algorithm. In our benchmark over 4 out-of-distribution datasets, distil-large-v3
outperformed distil-large-v2 by 5% WER average. Thus, you can expect significant performance gains by switching to this
latest checkpoint.
## Python Usage
To use the model in the original Whisper format, first ensure you have the [`openai-whisper`](https://pypi.org/project/openai-whisper/) package installed.
For this example, we'll also install 🤗 Datasets to load a toy audio dataset from the Hugging Face Hub:
```bash
pip install --upgrade pip
pip install --upgrade openai-whisper datasets[audio]
```
The following code-snippet demonstrates how to transcribe a sample file from the LibriSpeech dataset loaded using
🤗 Datasets:
```python
from huggingface_hub import hf_hub_download
from datasets import load_dataset
from whisper import load_model, transcribe
model_path = hf_hub_download(repo_id="distil-whisper/distil-large-v3-openai", filename="model.bin")
model = load_model(model_path)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
sample = dataset[0]["audio"]["path"]
pred_out = transcribe(model, audio=sample, language="en")
print(pred_out["text"])
```
Note that the model weights will be downloaded and saved to your cache the first time you run the example. Subsequently,
you can re-use the same example, and the weights will be loaded directly from your cache without having to download them
again.
To transcribe a local audio file, simply pass the path to the audio file as the `audio` argument to transcribe:
```python
pred_out = transcribe(model, audio="audio.mp3", language="en")
```
## CLI Usage
The Distil-Whisper model can also be used with the OpenAI Whisper CLI. First, pip install the Hugging Face Hub package:
```bash
pip install --upgrade huggingface_hub
```
Next, download the weights for distil-large-v3 locally:
```bash
huggingface-cli download distil-whisper/distil-large-v3-openai model.bin --local-dir distil-large-v3
```
Finally, use the OpenAI Whisper CLI to transcribe:
```bash
whisper audio.mp3 --model distil-large-v3/model.bin --language en
```
## Model Details
For more information about the distil-large-v3 model, refer to the original [model card](https://huggingface.co/distil-whisper/distil-large-v3).
## License
Distil-Whisper inherits the [MIT license](https://github.com/huggingface/distil-whisper/blob/main/LICENSE) from OpenAI's Whisper model.
## Citation
If you use this model, please consider citing the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430):
```
@misc{gandhi2023distilwhisper,
title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling},
author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush},
year={2023},
eprint={2311.00430},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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