Whisper Base CA
This model is a fine-tuned version of openai/whisper-base on the Common Accent dataset. It achieves the following results on the evaluation set:
- Loss: 0.7230
- Wer Ortho: 30.5998
- Wer: 0.2638
- Cer: 0.1320
- Precision: 0.8083
- Recall: 0.8233
- F1: 0.8150
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: 16
- 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 |
|---|---|---|---|---|---|---|---|---|---|
| 0.1367 | 0.8 | 500 | 0.7230 | 30.5998 | 0.2638 | 0.1320 | 0.8083 | 0.8233 | 0.8150 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Setosm/whisper-base-ca
Base model
openai/whisper-baseDataset used to train Setosm/whisper-base-ca
Evaluation results
- Wer on Common Accentself-reported0.264
- Precision on Common Accentself-reported0.808
- Recall on Common Accentself-reported0.823
- F1 on Common Accentself-reported0.815