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whisper-NST2
This model is a fine-tuned version of openai/whisper-small on the NBAILAB/NST - NO-CLOSE dataset. It achieves the following results on the evaluation set:
- Loss: 0.2990
- Wer: 7.7537
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: 4e-05
- train_batch_size: 96
- eval_batch_size: 16
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1846 | 0.1 | 1000 | 0.3460 | 14.9373 |
| 0.1325 | 0.2 | 2000 | 0.3413 | 11.4025 |
| 0.1135 | 0.3 | 3000 | 0.3428 | 12.6568 |
| 0.0955 | 0.4 | 4000 | 0.3140 | 10.7184 |
| 0.0871 | 0.5 | 5000 | 0.2907 | 9.4641 |
| 0.0774 | 0.6 | 6000 | 0.3019 | 11.4025 |
| 0.041 | 1.1 | 7000 | 0.2897 | 9.0080 |
| 0.0306 | 1.2 | 8000 | 0.3013 | 7.6397 |
| 0.0279 | 1.3 | 9000 | 0.2958 | 9.1220 |
| 0.0239 | 1.4 | 10000 | 0.2990 | 7.7537 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
