whisper-tiny-final
This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0714
- Wer: 6.3947
Model description
Step Training Loss Validation Loss Wer 1000 0.727300 0.734777 71.347666 2000 0.392000 0.430395 52.059163 3000 0.317100 0.305939 39.781162 4000 0.206400 0.225029 30.785726 5000 0.152800 0.169434 23.076923 6000 0.119000 0.130408 16.517293 7000 0.082300 0.102279 11.755650 8000 0.079600 0.085155 8.511574 9000 0.051400 0.075068 7.048991 10000 0.045000 0.071429 6.394678
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: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.7273 | 1.6051 | 1000 | 0.7348 | 71.3477 |
| 0.392 | 3.2103 | 2000 | 0.4304 | 52.0592 |
| 0.3171 | 4.8154 | 3000 | 0.3059 | 39.7812 |
| 0.2064 | 6.4205 | 4000 | 0.2250 | 30.7857 |
| 0.1528 | 8.0257 | 5000 | 0.1694 | 23.0769 |
| 0.119 | 9.6308 | 6000 | 0.1304 | 16.5173 |
| 0.0823 | 11.2360 | 7000 | 0.1023 | 11.7556 |
| 0.0796 | 12.8411 | 8000 | 0.0852 | 8.5116 |
| 0.0514 | 14.4462 | 9000 | 0.0751 | 7.0490 |
| 0.045 | 16.0514 | 10000 | 0.0714 | 6.3947 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.0
- Datasets 2.19.1
- Tokenizers 0.19.1