whisper-small-clearglobal-hausa-asr-1.0.0

This model is a fine-tuned version of openai/whisper-small on the TWB Voice 1.0 dataset.

It achieves the following results on the internal evaluation set:

  • WER: 2.23%
  • CER: 3.11%

Training and evaluation data

This model was trained by colleagues from the Makerere University Centre for Artificial Intelligence and Data Science in collaboration with CLEAR Global. We gratefully acknowledge their expertise and partnership.

Model was trained and tested on the approved Hausa subset of TWB Voice 1.0 dataset.

Train/dev/test portions correspond to the splits in this dataset version. Test splits consist of speakers not present in train and dev splits.

We also tested on external datasets: Common voice v17 and Naija Voices test splits.

The evaluation results are as follows:

Evaluation dataset WER (%) CER (%)
TWB Voice 1.0 2.23 3.11
Common Voice v17 Hausa
Naija Voices Hausa

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.08
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.8668 1.0 423 0.3216 0.2559 0.0794
0.2265 2.0 846 0.2439 0.1971 0.0668
0.1419 3.0 1269 0.2218 0.1832 0.0721
0.1109 4.0 1692 0.2233 0.1639 0.0639
0.088 5.0 2115 0.2168 0.1488 0.0578
0.0591 6.0 2538 0.1909 0.1358 0.0548
0.0438 7.0 2961 0.1867 0.1165 0.0488
0.033 8.0 3384 0.1855 0.1190 0.0510
0.028 9.0 3807 0.1898 0.1177 0.0525
0.0223 10.0 4230 0.1833 0.1139 0.0522
0.0182 11.0 4653 0.1835 0.1090 0.0469
0.0148 12.0 5076 0.1771 0.1111 0.0517
0.0117 13.0 5499 0.1726 0.1047 0.0479
0.0112 14.0 5922 0.1698 0.0942 0.0425
0.0102 15.0 6345 0.1691 0.0940 0.0413
0.0099 16.0 6768 0.1701 0.0960 0.0456
0.0093 17.0 7191 0.1793 0.1000 0.0456
0.0082 18.0 7614 0.1724 0.0957 0.0443
0.0065 19.0 8037 0.1726 0.0989 0.0465

Framework versions

  • Transformers 4.53.1
  • Pytorch 2.7.1+cu128
  • Datasets 4.0.0
  • Tokenizers 0.21.2
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