whisper-medium-bem
This model is a fine-tuned version of openai/whisper-medium on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3519
- Wer: 33.5877
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.6509 | 0.34 | 500 | 0.4872 | 50.9031 |
| 0.5212 | 0.67 | 1000 | 0.3972 | 40.5156 |
| 0.3957 | 1.01 | 1500 | 0.3451 | 36.4793 |
| 0.2956 | 1.34 | 2000 | 0.3421 | 37.3866 |
| 0.2987 | 1.68 | 2500 | 0.3206 | 34.5374 |
| 0.1665 | 2.02 | 3000 | 0.3290 | 34.1135 |
| 0.1557 | 2.35 | 3500 | 0.3334 | 35.0462 |
| 0.1345 | 2.69 | 4000 | 0.3374 | 33.8506 |
| 0.0617 | 3.02 | 4500 | 0.3445 | 33.6216 |
| 0.0661 | 3.36 | 5000 | 0.3519 | 33.5877 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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Evaluation results
- WER on bembaspeechtest set self-reported34.840
- WER on BembaSpeechtest set self-reported34.840