Whisper Base Noise Ko - Dearlie
This model is a fine-tuned version of openai/whisper-base on the Noise Data dataset. It achieves the following results on the evaluation set:
- Loss: 1.3670
- Cer: 57.4924
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 |
|---|---|---|---|---|
| 1.6034 | 0.8780 | 1000 | 1.6217 | 75.3884 |
| 1.4053 | 1.7559 | 2000 | 1.4598 | 60.7893 |
| 1.2681 | 2.6339 | 3000 | 1.3881 | 61.1636 |
| 1.1608 | 3.5119 | 4000 | 1.3670 | 57.4924 |
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for Dearlie/whisper-noise2
Base model
openai/whisper-base