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whisper-base-zh-20230711 - au2a
This model is a fine-tuned version of openai/whisper-base on the some hakka audio dataset. It achieves the following results on the evaluation set:
- Loss: 0.4551
- Cer: 16.9978
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: 32
- 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: 500
- training_steps: 10000
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.4673 | 0.65 | 1000 | 0.6526 | 25.0548 |
| 0.2203 | 1.29 | 2000 | 0.4985 | 19.8459 |
| 0.1446 | 1.94 | 3000 | 0.4557 | 18.0026 |
| 0.0956 | 2.59 | 4000 | 0.4438 | 16.9676 |
| 0.0527 | 3.24 | 5000 | 0.4450 | 17.0998 |
| 0.0423 | 3.88 | 6000 | 0.4441 | 17.7797 |
| 0.027 | 4.53 | 7000 | 0.4474 | 16.9260 |
| 0.0177 | 5.18 | 8000 | 0.4515 | 16.5861 |
| 0.0165 | 5.83 | 9000 | 0.4537 | 16.8392 |
| 0.0129 | 6.47 | 10000 | 0.4551 | 16.9978 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.11.0+cu113
- Datasets 2.13.1
- Tokenizers 0.13.3
