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whisper-base
This model is a fine-tuned version of openai/whisper-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2522
- Wer: 23.1797
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: 64
- 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: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.1114 | 0.0 | 1 | 2.3698 | 75.1864 |
| 0.3272 | 0.29 | 1000 | 0.4182 | 37.7505 |
| 0.251 | 0.58 | 2000 | 0.3408 | 30.9679 |
| 0.2207 | 0.88 | 3000 | 0.3059 | 28.3058 |
| 0.1779 | 1.17 | 4000 | 0.2890 | 26.7555 |
| 0.1691 | 1.46 | 5000 | 0.2742 | 25.2099 |
| 0.1622 | 1.75 | 6000 | 0.2645 | 24.6840 |
| 0.1397 | 2.04 | 7000 | 0.2587 | 23.8812 |
| 0.1394 | 2.34 | 8000 | 0.2562 | 23.6586 |
| 0.1361 | 2.63 | 9000 | 0.2536 | 23.4633 |
| 0.1356 | 2.92 | 10000 | 0.2522 | 23.1797 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
