whisper-a-norm-ls-8
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1538
- Wer: 78.3845
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: 0.0004
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 132
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 1.0 | 70 | 0.2366 | 16.4960 |
| 0.7791 | 2.0 | 140 | 0.4579 | 96.5870 |
| 0.8786 | 3.0 | 210 | 0.2974 | 91.4676 |
| 0.8786 | 4.0 | 280 | 0.2770 | 93.1741 |
| 0.2773 | 5.0 | 350 | 0.2503 | 91.8089 |
| 0.2596 | 6.0 | 420 | 0.3236 | 95.2218 |
| 0.2596 | 7.0 | 490 | 0.1855 | 93.0603 |
| 0.2108 | 7.8921 | 552 | 0.1538 | 78.3845 |
Framework versions
- Transformers 4.47.0.dev0
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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Base model
openai/whisper-small