stammer-small
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: 0.4497
- Wer: 35.7430
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.9039 | 0.3968 | 25 | 0.7568 | 42.1687 |
| 0.4742 | 0.7937 | 50 | 0.4766 | 35.9438 |
| 0.3073 | 1.1905 | 75 | 0.4589 | 35.1406 |
| 0.3061 | 1.5873 | 100 | 0.4497 | 35.7430 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for stillerman/stammer-small
Base model
openai/whisper-small