<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
whisper-base-zh-20230721 - 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: 0.4546
- Cer: 16.5974
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 |
|---|---|---|---|---|
| 0.4669 | 0.65 | 1000 | 0.6528 | 25.0548 |
| 0.2208 | 1.29 | 2000 | 0.5006 | 19.8761 |
| 0.1452 | 1.94 | 3000 | 0.4546 | 17.9497 |
| 0.0951 | 2.59 | 4000 | 0.4431 | 17.4511 |
| 0.0526 | 3.24 | 5000 | 0.4450 | 17.3113 |
| 0.0422 | 3.88 | 6000 | 0.4440 | 16.6201 |
| 0.0271 | 4.53 | 7000 | 0.4471 | 17.0658 |
| 0.0179 | 5.18 | 8000 | 0.4509 | 16.5823 |
| 0.0166 | 5.83 | 9000 | 0.4535 | 16.8543 |
| 0.0129 | 6.47 | 10000 | 0.4546 | 16.5974 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
