---
license: apache-2.0
language:
- sw
tags:
- hf-asr-leaderboard
- generated-from-trainer
datasets:
- mozilla_foundation/common_voice_11_0
metrics:
- WER
---
## Model
* Name: Whisper Large-v2 Swahili
* Description: Fine-tuned Whisper weights for speech-to-text task.
* Dataset:
  - Train and validation splits for Swahili subsets of [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0).
  - Train, validation and test splits for Swahili subsets of [Google Fleurs](https://huggingface.co/datasets/google/fleurs/). 
* Performance: **19.887087 WER**

## Weights
* Date of release: 12.09.2022
* Size:
* License: MIT

## Usage
To use these weights in HuggingFace's `transformers` library, you can do the following:
```python
from transformers import WhisperForConditionalGeneration

model = WhisperForConditionalGeneration.from_pretrained("hedronstone/whisper-large-v2-sw")
```