metadata
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
- precision
- recall
- f1
model-index:
- name: ./whisper-base-ea_base
results: []
./whisper-base-ea_base
This model is a fine-tuned version of openai/whisper-base on the Afrispeech-200 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6880
- Wer Ortho: 0.2755
- Wer: 0.2202
- Cer: 0.0998
- Precision: 0.8628
- Recall: 0.8622
- F1: 0.8616
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: 8
- seed: 42
- 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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | Cer | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|---|---|
| 0.9241 | 0.4237 | 100 | 0.8794 | 0.2973 | 0.2468 | 0.1060 | 0.8428 | 0.8469 | 0.8443 |
| 0.7528 | 0.8475 | 200 | 0.7464 | 0.2903 | 0.2354 | 0.1032 | 0.8583 | 0.8593 | 0.8581 |
| 0.5275 | 1.2712 | 300 | 0.7158 | 0.2778 | 0.2285 | 0.1000 | 0.8619 | 0.8627 | 0.8616 |
| 0.5686 | 1.6949 | 400 | 0.6956 | 0.2805 | 0.2255 | 0.1021 | 0.8638 | 0.8632 | 0.8626 |
| 0.3472 | 2.1186 | 500 | 0.6880 | 0.2755 | 0.2202 | 0.0998 | 0.8628 | 0.8622 | 0.8616 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1