Downloads · 30 days
11
2% of all-time downloads
whisper-tiny-yoruba
EYEDOL/whisper-tiny-yoruba
whisper-tiny-yoruba is a automatic speech recognition model from EYEDOL. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
601
Public
Parameters
37.8M
6.2 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors151 MB · 97%
At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- whisper
- Access
- Public
- Created
- Jun 26, 2026
- Updated
- Jun 28, 2026
- SHA
- 91f92a34
Model card
From the Hugging Face model README
<!-- 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. -->
EYEDOL/whisper-tiny-yoruba
This model is a fine-tuned version of EYEDOL/whisper-tiny-yoruba on the EYEDOL/naija-voices-yoruba-split_0-1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.8526
- Wer Ortho: 0.7773
- Wer: 0.7006
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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 12
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 1.7353 | 1.0 | 583 | 0.8680 | 0.9448 | 0.8654 |
| 1.6436 | 2.0 | 1166 | 0.8494 | 0.8213 | 0.7490 |
| 1.5173 | 3.0 | 1749 | 0.8319 | 0.8237 | 0.7470 |
| 1.4143 | 4.0 | 2332 | 0.8215 | 0.7845 | 0.7128 |
| 1.3252 | 5.0 | 2915 | 0.8135 | 0.8788 | 0.7910 |
| 1.2425 | 6.0 | 3498 | 0.8106 | 0.7988 | 0.7224 |
| 1.1664 | 7.0 | 4081 | 0.8118 | 0.8508 | 0.7635 |
| 1.0950 | 8.0 | 4664 | 0.8156 | 0.7628 | 0.6813 |
| 1.0273 | 9.0 | 5247 | 0.8191 | 0.7867 | 0.7204 |
| 0.9611 | 10.0 | 5830 | 0.8292 | 0.7736 | 0.6948 |
| 0.8975 | 11.0 | 6413 | 0.8353 | 0.8007 | 0.7126 |
| 0.8363 | 12.0 | 6996 | 0.8526 | 0.7773 | 0.7006 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Languages
- yo
- Created
- Jun 26, 2026
- Updated
- Jun 28, 2026