waxal-whisper_small_multi_s42
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: 0.4327
- Wer: 0.7159
- Cer: 0.3848
- Score: 0.5504
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_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Score |
|---|---|---|---|---|---|---|
| 0.5996 | 0.5496 | 1000 | 0.5983 | 0.8222 | 0.4107 | 0.6165 |
| 0.4298 | 1.0989 | 2000 | 0.4846 | 0.5734 | 0.2759 | 0.4246 |
| 0.3923 | 1.6485 | 3000 | 0.4546 | 0.5380 | 0.2517 | 0.3949 |
| 0.2835 | 2.1979 | 4000 | 0.4413 | 0.4935 | 0.2347 | 0.3641 |
| 0.3116 | 2.7475 | 5000 | 0.4295 | 0.6399 | 0.3235 | 0.4817 |
| 0.2426 | 3.2968 | 6000 | 0.4358 | 0.6338 | 0.3496 | 0.4917 |
| 0.2553 | 3.8464 | 7000 | 0.4327 | 0.7159 | 0.3848 | 0.5504 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.10.0+cu128
- Datasets 3.6.0
- Tokenizers 0.22.2