whisper-base-af-fleurs-norm – AI Model by deepdml | AlphaNeural AI

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deepdml/
whisper-base-af-fleurs-norm

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openai/whisper-base

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Whisper Base af

This model is a fine-tuned version of openai/whisper-base on the Fleurs dataset. It achieves the following results on the evaluation set:

Loss: 1.1171
Wer: 42.0087
Cer: 18.5731

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: 64
eval_batch_size: 64
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
0.6138	3.0017	100	1.0982	48.7792	19.1919
0.2216	6.0033	200	1.0553	43.0303	17.0425
0.0984	9.005	300	1.0633	44.5195	19.8927
0.0527	12.0067	400	1.0950	45.1429	19.2446
0.0325	15.0083	500	1.1121	41.8182	16.4179
0.0291	18.01	600	1.1171	42.0087	18.5731

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