|
--- |
|
license: apache-2.0 |
|
language: |
|
- sw |
|
tags: |
|
- hf-asr-leaderboard |
|
- generated-from-trainer |
|
- whisper-event |
|
datasets: |
|
- mozilla_foundation/common_voice_11_0 |
|
metrics: |
|
- WER |
|
--- |
|
## Model |
|
* Name: Whisper Large-v2 Swahili |
|
* Description: Whisper weights for speech-to-text task, fine-tuned and evaluated on normalized data. |
|
* 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 |
|
* 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") |
|
``` |
|
|
