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_8.5hr
results: []
./whisper-base-ea_8.5hr
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: 0.7306
- Wer Ortho: 0.2818
- Wer: 0.2261
- Cer: 0.1004
- Precision: 0.8516
- Recall: 0.8552
- F1: 0.8527
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 |
|---|---|---|---|---|---|---|---|---|---|
| 1.0157 | 0.1825 | 100 | 0.9950 | 0.3122 | 0.2683 | 0.1220 | 0.8325 | 0.8364 | 0.8338 |
| 0.8347 | 0.3650 | 200 | 0.8313 | 0.3056 | 0.2562 | 0.1182 | 0.8429 | 0.8450 | 0.8433 |
| 0.6958 | 0.5474 | 300 | 0.7851 | 0.2905 | 0.2393 | 0.1060 | 0.8478 | 0.8503 | 0.8484 |
| 0.8021 | 0.7299 | 400 | 0.7532 | 0.2848 | 0.2286 | 0.1014 | 0.8541 | 0.8563 | 0.8546 |
| 0.8634 | 0.9124 | 500 | 0.7306 | 0.2818 | 0.2261 | 0.1004 | 0.8516 | 0.8552 | 0.8527 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1