Whisper Small German SBB all SNR - v2
This model is a fine-tuned version of openai/whisper-small on the SBB Dataset 05.01.2023 dataset. It achieves the following results on the evaluation set:
- Loss: 0.7183
- Wer: 1.8738
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-06
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 4.5636 | 0.71 | 100 | 2.7931 | 1.1541 |
| 1.4736 | 1.42 | 200 | 0.8866 | 1.0444 |
| 0.8446 | 2.13 | 300 | 0.9127 | 1.5136 |
| 0.7396 | 2.84 | 400 | 0.7580 | 1.2644 |
| 0.7699 | 3.55 | 500 | 0.7183 | 1.8738 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.12.1
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