Whisper Small
This model is a fine-tuned version of openai/whisper-small on the Speechocean762_CMUkids_Myst dataset. It achieves the following results on the evaluation set:
- Loss: 0.4056
- Wer: 18.0759
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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2048
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 4.9694 | 1.8398 | 500 | 3.0150 | 18.1672 |
| 3.1539 | 3.6777 | 1000 | 1.8826 | 16.9380 |
| 1.7307 | 5.5157 | 1500 | 1.0186 | 17.3722 |
| 0.7948 | 7.3536 | 2000 | 0.4598 | 18.7217 |
| 0.6236 | 9.1915 | 2500 | 0.4215 | 19.6081 |
| 0.6026 | 11.0295 | 3000 | 0.4111 | 17.6885 |
| 0.5911 | 12.8692 | 3500 | 0.4069 | 18.4567 |
| 0.6070 | 14.7072 | 4000 | 0.4056 | 18.0759 |
Framework versions
- Transformers 5.8.1
- Pytorch 2.5.1+cu121
- Datasets 4.8.5
- Tokenizers 0.22.2