Whisper Small En - Jithu J
This model is a fine-tuned version of openai/whisper-small on the medical-speech-transcription-and-intent dataset. It achieves the following results on the evaluation set:
- Loss: 0.0931
- Wer: 3.9012
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0165 | 3.3898 | 1000 | 0.0971 | 4.7860 |
| 0.0012 | 6.7797 | 2000 | 0.0905 | 4.1425 |
| 0.0001 | 10.1695 | 3000 | 0.0930 | 4.0138 |
| 0.0001 | 13.5593 | 4000 | 0.0931 | 3.9012 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3