---
language:
- af
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Medium af
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Fleurs
      type: google/fleurs
      config: af_za
      split: test
      args: af_za
    metrics:
    - name: Wer
      type: wer
      value: 24.12121212121212
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Medium af

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6584
- Wer: 24.1212
- Cer: 9.2133

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.04
- training_steps: 600

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     | Cer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|
| 0.322         | 0.1667 | 100  | 0.6194          | 25.8355 | 9.9346  |
| 0.0922        | 1.1183 | 200  | 0.6106          | 25.8528 | 10.0431 |
| 0.0363        | 2.07   | 300  | 0.6271          | 24.5714 | 10.3715 |
| 0.019         | 3.0217 | 400  | 0.6469          | 24.8831 | 10.5211 |
| 0.011         | 3.1883 | 500  | 0.6518          | 27.1861 | 11.8553 |
| 0.0056        | 4.14   | 600  | 0.6584          | 24.1212 | 9.2133  |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

## Citation

Please cite the model using the following BibTeX entry:

```bibtex
@misc{deepdml/whisper-medium-af-fleurs-norm,
      title={Fine-tuned Whisper medium ASR model for speech recognition in Afrikaans},
      author={Jimenez, David},
      howpublished={\url{https://huggingface.co/deepdml/whisper-medium-af-fleurs-norm}},
      year={2026}
    }
```