whisper-lt-finetune
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2550
- Wer: 13.5797
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: 250
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1556 | 0.97 | 1000 | 0.2354 | 15.2781 |
| 0.0709 | 1.95 | 2000 | 0.2336 | 14.6419 |
| 0.0259 | 2.92 | 3000 | 0.2415 | 14.0186 |
| 0.0098 | 3.89 | 4000 | 0.2496 | 13.7355 |
| 0.0056 | 4.87 | 5000 | 0.2550 | 13.5797 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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