Whisper Tiny Hakka Condenser
This model is a fine-tuned version of openai/whisper-tiny on the HAT ASR Aligned dataset. It achieves the following results on the evaluation set:
- Loss: 0.1729
- Cer: 10.2307
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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1521
- training_steps: 15210
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.2476 | 0.9993 | 1521 | 0.4437 | 23.6551 |
| 0.0892 | 1.9987 | 3042 | 0.2482 | 14.6693 |
| 0.0543 | 2.9980 | 4563 | 0.2007 | 11.1774 |
| 0.0361 | 3.9974 | 6084 | 0.1847 | 12.4939 |
| 0.0235 | 4.9967 | 7605 | 0.1791 | 10.5405 |
| 0.0157 | 5.9961 | 9126 | 0.1727 | 10.9000 |
| 0.0121 | 6.9954 | 10647 | 0.1724 | 11.1554 |
| 0.0082 | 7.9947 | 12168 | 0.1720 | 10.3694 |
| 0.0059 | 8.9941 | 13689 | 0.1732 | 10.4053 |
| 0.0049 | 9.9934 | 15210 | 0.1729 | 10.2307 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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