Whisper Base ig
This model is a fine-tuned version of openai/whisper-base on the google/fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 1.7179
- Wer: 93.3804
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2396 | 0.2 | 1000 | 1.3704 | 57.7791 |
| 0.0803 | 1.0814 | 2000 | 1.5414 | 71.3104 |
| 0.0636 | 1.2814 | 3000 | 1.6047 | 94.5668 |
| 0.0346 | 2.1628 | 4000 | 1.6904 | 83.7003 |
| 0.035 | 3.0442 | 5000 | 1.7179 | 93.3804 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
Citation
@misc{deepdml/whisper-base-ig-mix,
title={Fine-tuned Whisper base ASR model for speech recognition in Igbo},
author={Jimenez, David},
howpublished={\url{https://huggingface.co/deepdml/whisper-base-ig-mix}},
year={2025}
}