whisper-medium-ne-en-finetuned
This model is a fine-tuned version of openai/whisper-medium on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2102
- Wer: 38.3318
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_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: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4435 | 0.4987 | 190 | 0.4008 | 56.4600 |
| 0.2822 | 0.9974 | 380 | 0.2786 | 46.0335 |
| 0.2132 | 1.4961 | 570 | 0.2409 | 42.7297 |
| 0.1847 | 1.9948 | 760 | 0.2153 | 40.3115 |
| 0.1176 | 2.4934 | 950 | 0.2113 | 39.1056 |
| 0.1111 | 2.9921 | 1140 | 0.2046 | 38.4272 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 2.21.0
- Tokenizers 0.22.1