Mal_ASR_Whisper_small_imasc_1000
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0642
- Wer: 52.2853
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: 32
- 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: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3098 | 0.74 | 200 | 0.2613 | 200.6810 |
| 0.1009 | 1.48 | 400 | 0.0988 | 54.5952 |
| 0.0559 | 2.22 | 600 | 0.0722 | 44.6184 |
| 0.0518 | 2.96 | 800 | 0.0608 | 39.1631 |
| 0.0285 | 3.7 | 1000 | 0.0573 | 46.0858 |
| 0.0166 | 4.44 | 1200 | 0.0567 | 46.7036 |
| 0.0082 | 5.19 | 1400 | 0.0589 | 50.9513 |
| 0.0075 | 5.93 | 1600 | 0.0590 | 65.6252 |
| 0.0031 | 6.67 | 1800 | 0.0629 | 57.2913 |
| 0.0018 | 7.41 | 2000 | 0.0642 | 52.2853 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.0