WhisperForSpokenNER-end2end
This model is a fine-tuned version of openai/whisper-small on the facebook/voxpopuli de+es+fr+nl dataset. It achieves the following results on the evaluation set:
- Loss: 0.2755
- Combined Wer: 0.1491
- F1 Score: 0.7163
- Label F1: 0.8200
- Wer: 0.0858
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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Combined Wer | F1 Score | Label F1 | Wer |
|---|---|---|---|---|---|---|---|
| 0.3252 | 0.1 | 500 | 0.3396 | 0.1918 | 0.6148 | 0.7578 | 0.1193 |
| 0.2729 | 0.2 | 1000 | 0.3158 | 0.1730 | 0.6449 | 0.7907 | 0.1058 |
| 0.2369 | 0.3 | 1500 | 0.2971 | 0.1736 | 0.6917 | 0.8083 | 0.1067 |
| 0.1967 | 0.4 | 2000 | 0.2823 | 0.1634 | 0.6915 | 0.8095 | 0.0999 |
| 0.1623 | 0.5 | 2500 | 0.2804 | 0.1693 | 0.7088 | 0.8249 | 0.1052 |
| 0.1146 | 1.02 | 3000 | 0.2820 | 0.1593 | 0.7012 | 0.8106 | 0.0951 |
| 0.0938 | 1.12 | 3500 | 0.2792 | 0.1500 | 0.7205 | 0.8238 | 0.0875 |
| 0.1001 | 1.22 | 4000 | 0.2750 | 0.1549 | 0.7072 | 0.8061 | 0.0928 |
| 0.0848 | 1.32 | 4500 | 0.2741 | 0.1471 | 0.7243 | 0.8318 | 0.0860 |
| 0.0649 | 1.42 | 5000 | 0.2745 | 0.1468 | 0.7304 | 0.8350 | 0.0858 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1