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Whisper Tiny GPU test
This model is a fine-tuned version of openai/whisper-tiny on the NbAiLab/NCC_S3 dataset. It achieves the following results on the evaluation set:
- Loss: 0.9375
- Wer: 51.3703
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: 3e-06
- train_batch_size: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 200
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.4574 | 0.1 | 200 | 1.4663 | 71.6504 |
| 1.9587 | 0.2 | 400 | 1.2581 | 64.7381 |
| 1.816 | 0.3 | 600 | 1.1672 | 60.9318 |
| 1.7199 | 0.4 | 800 | 1.1006 | 57.6736 |
| 1.6686 | 0.5 | 1000 | 1.0630 | 56.1815 |
| 1.621 | 0.6 | 1200 | 1.0273 | 55.4811 |
| 1.5846 | 0.7 | 1400 | 1.0017 | 53.9890 |
| 1.5482 | 0.8 | 1600 | 0.9773 | 53.0146 |
| 1.521 | 0.9 | 1800 | 0.9575 | 52.1011 |
| 1.4932 | 1.0 | 2000 | 0.9375 | 51.3703 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
