Whisper Large-v2 German SBB ASR
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: 0.0277
- Wer: 0.0235
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: 32
- 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: 600
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
1.3449 | 0.36 | 100 | 0.2158 | 0.0387 |
0.0647 | 0.71 | 200 | 0.0266 | 0.0197 |
0.0308 | 1.07 | 300 | 0.0315 | 0.0216 |
0.0188 | 1.42 | 400 | 0.0286 | 0.0197 |
0.0136 | 1.78 | 500 | 0.0298 | 0.0209 |
0.0089 | 2.14 | 600 | 0.0277 | 0.0235 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.12.1
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