whisper_input_decoder_shift_r_labels_no_force__0015
This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 3.3069
- Train Accuracy: 0.0148
- Train Wermet: 0.6961
- Validation Loss: 3.1102
- Validation Accuracy: 0.0124
- Validation Wermet: 0.7609
- Epoch: 14
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|---|---|---|---|---|---|---|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3