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whisper-base-zh-20230718-1 - au2a
This model is a fine-tuned version of openai/whisper-small on the some hakka audio dataset. It achieves the following results on the evaluation set:
- Loss: 0.4142
- Cer: 84.7926
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: 5e-06
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.0499 | 2.59 | 1000 | 0.3377 | 153.9019 |
| 0.0035 | 5.17 | 2000 | 0.3506 | 138.4528 |
| 0.0015 | 7.76 | 3000 | 0.3651 | 128.2541 |
| 0.001 | 10.35 | 4000 | 0.3754 | 105.1522 |
| 0.0005 | 12.94 | 5000 | 0.3841 | 90.0846 |
| 0.0004 | 15.52 | 6000 | 0.3925 | 92.5134 |
| 0.0002 | 18.11 | 7000 | 0.4011 | 86.3035 |
| 0.0002 | 20.7 | 8000 | 0.4070 | 80.0219 |
| 0.0001 | 23.29 | 9000 | 0.4118 | 82.5451 |
| 0.0001 | 25.87 | 10000 | 0.4142 | 84.7926 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
