whisper-NST2-unfreeze-constanti-low-lr
This model is a fine-tuned version of openai/whisper-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3562
- Wer: 8.5519
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: 96
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 20000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1901 | 0.05 | 1000 | 0.3069 | 14.8233 |
| 0.1323 | 0.1 | 2000 | 0.2687 | 11.2885 |
| 0.1137 | 0.15 | 3000 | 0.2620 | 10.8324 |
| 0.1022 | 0.2 | 4000 | 0.2976 | 9.0080 |
| 0.0937 | 0.25 | 5000 | 0.2584 | 9.5781 |
| 0.0875 | 0.3 | 6000 | 0.2704 | 20.2965 |
| 0.0592 | 1.05 | 7000 | 0.2751 | 9.0080 |
| 0.0488 | 1.1 | 8000 | 0.2778 | 8.6659 |
| 0.0475 | 1.15 | 9000 | 0.2792 | 9.4641 |
| 0.0439 | 1.2 | 10000 | 0.2880 | 8.3238 |
| 0.0425 | 1.25 | 11000 | 0.2954 | 8.5519 |
| 0.0416 | 1.3 | 12000 | 0.2896 | 20.2965 |
| 0.0289 | 2.05 | 13000 | 0.2990 | 7.9818 |
| 0.0229 | 2.1 | 14000 | 0.3027 | 7.4116 |
| 0.0248 | 2.15 | 15000 | 0.2968 | 8.6659 |
| 0.0225 | 2.2 | 16000 | 0.3100 | 8.5519 |
| 0.0222 | 2.25 | 17000 | 0.3132 | 9.3501 |
| 0.0219 | 2.3 | 18000 | 0.3230 | 7.6397 |
| 0.0162 | 3.04 | 19000 | 0.3380 | 9.8062 |
| 0.0132 | 3.09 | 20000 | 0.3562 | 8.5519 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1