whisper-large-cit-do0.2-wd0.001-tr5
This model is a fine-tuned version of openai/whisper-large-v3 on the SF 200 dataset. It achieves the following results on the evaluation set:
- Loss: 0.8701
- Wer: 28.8330
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: 5e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 200
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.0205 | 0.8889 | 10 | 0.8452 | 34.3249 |
| 0.7299 | 1.7778 | 20 | 0.6455 | 31.1213 |
| 0.48 | 2.6667 | 30 | 0.5552 | 30.4348 |
| 0.291 | 3.5556 | 40 | 0.5288 | 30.6636 |
| 0.1931 | 4.4444 | 50 | 0.5479 | 28.3753 |
| 0.107 | 5.3333 | 60 | 0.6104 | 29.0618 |
| 0.0622 | 6.2222 | 70 | 0.6509 | 28.8330 |
| 0.0271 | 7.1111 | 80 | 0.7900 | 30.4348 |
| 0.0198 | 8.0 | 90 | 0.7246 | 30.2059 |
| 0.0176 | 8.8889 | 100 | 0.6992 | 27.6888 |
| 0.0163 | 9.7778 | 110 | 0.7896 | 29.5195 |
| 0.0087 | 10.6667 | 120 | 0.7793 | 30.4348 |
| 0.0092 | 11.5556 | 130 | 0.8213 | 28.8330 |
| 0.0063 | 12.4444 | 140 | 0.8369 | 29.5195 |
| 0.0038 | 13.3333 | 150 | 0.8262 | 29.7483 |
| 0.0036 | 14.2222 | 160 | 0.8506 | 28.3753 |
| 0.0021 | 15.1111 | 170 | 0.8647 | 29.5195 |
| 0.0017 | 16.0 | 180 | 0.8608 | 29.5195 |
| 0.0012 | 16.8889 | 190 | 0.8662 | 28.8330 |
| 0.001 | 17.7778 | 200 | 0.8701 | 28.8330 |
Framework versions
- Transformers 4.41.1
- Pytorch 1.13.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
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