Model description
openai/whisper-tiny
Intended uses & limitations
automatic-speech-recognition
Training and evaluation data
PolyAI/minds14
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer Ortho, Wer| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|
| 5.6090 | 0.8929 | 25 | 1.9933 | 0.4775 | 0.3867 |
| 1.0683 | 1.7857 | 50 | 0.5234 | 0.3812 | 0.3701 |
| 0.6299 | 2.6786 | 75 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2