whisper-small-finetuned
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: 0.5379
- Wer: 0.3018
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.0395 | 1.0 | 4 | 0.6939 | 0.3136 |
| 0.5944 | 2.0 | 8 | 0.5685 | 0.3107 |
| 0.2080 | 3.0 | 12 | 0.5175 | 0.3047 |
| 0.0648 | 4.0 | 16 | 0.5379 | 0.3018 |
| 0.0293 | 5.0 | 20 | 0.5901 | 0.3047 |
| 0.0156 | 6.0 | 24 | 0.6204 | 0.3195 |
Framework versions
- Transformers 5.14.1
- Pytorch 2.10.0+cu128
- Datasets 5.0.1
- Tokenizers 0.22.2