Whisper Small German SBB all SNR - v4
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: 0.0287
- Wer: 0.0222
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-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: 700
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.6894 | 0.71 | 100 | 0.4702 | 0.4661 |
| 0.1896 | 1.42 | 200 | 0.0322 | 0.0241 |
| 0.0297 | 2.13 | 300 | 0.0349 | 0.0228 |
| 0.0181 | 2.84 | 400 | 0.0250 | 0.0209 |
| 0.0154 | 3.55 | 500 | 0.0298 | 0.0209 |
| 0.0112 | 4.26 | 600 | 0.0327 | 0.0222 |
| 0.009 | 4.96 | 700 | 0.0287 | 0.0222 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.12.1
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