Glaswegian_Whisper_001
This model is a fine-tuned version of openai/whisper-small on the Glaswegian audio dataset. It achieves the following results on the evaluation set:
- Loss: 0.7977
- Wer: 22.3992
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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0012 | 25.6410 | 1000 | 0.7225 | 22.7176 |
| 0.0062 | 51.2821 | 2000 | 0.7507 | 22.8238 |
| 0.0001 | 76.9231 | 3000 | 0.7888 | 22.8238 |
| 0.0 | 102.5641 | 4000 | 0.7977 | 22.3992 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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