Whisper-finetune_all
This model is a fine-tuned version of openai/whisper-large-v2 on the Amitabha_all dataset. It achieves the following results on the evaluation set:
- Loss: 0.0003
- Cer: 0.2260
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: 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.1067 | 2.5253 | 1000 | 0.0800 | 11.4694 |
| 0.0133 | 5.0505 | 2000 | 0.0102 | 3.3448 |
| 0.0017 | 7.5758 | 3000 | 0.0014 | 0.3232 |
| 0.0002 | 10.1010 | 4000 | 0.0003 | 0.2260 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Base model
openai/whisper-large-v2