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whisper-medium-NST-uf-linlr
This model is a fine-tuned version of openai/whisper-medium on the NBAILAB/NST - NO-CLOSE dataset. It achieves the following results on the evaluation set:
- Loss: 0.3007
- Wer: 9.1220
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: 72
- 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: 1000
- training_steps: 20000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2046 | 0.05 | 1000 | 0.3426 | 15.2794 |
| 0.148 | 0.1 | 2000 | 0.3284 | 10.8324 |
| 0.121 | 0.15 | 3000 | 0.3092 | 12.8848 |
| 0.1089 | 0.2 | 4000 | 0.2808 | 10.4903 |
| 0.0976 | 0.25 | 5000 | 0.2617 | 9.9202 |
| 0.0901 | 0.3 | 6000 | 0.2604 | 21.8928 |
| 0.0834 | 0.35 | 7000 | 0.2877 | 9.3501 |
| 0.0825 | 0.4 | 8000 | 0.2794 | 9.3501 |
| 0.0553 | 1.05 | 9000 | 0.2845 | 9.5781 |
| 0.0472 | 1.1 | 10000 | 0.2814 | 24.1733 |
| 0.0409 | 1.15 | 11000 | 0.3084 | 8.0958 |
| 0.041 | 1.2 | 12000 | 0.2865 | 9.2360 |
| 0.0353 | 1.25 | 13000 | 0.2828 | 6.4994 |
| 0.0348 | 1.3 | 14000 | 0.2708 | 7.5257 |
| 0.0349 | 1.35 | 15000 | 0.2842 | 23.0331 |
| 0.0361 | 1.4 | 16000 | 0.2769 | 10.1482 |
| 0.0249 | 2.04 | 17000 | 0.2935 | 8.8940 |
| 0.0204 | 2.09 | 18000 | 0.2874 | 12.4287 |
| 0.0175 | 2.14 | 19000 | 0.2882 | 12.9989 |
| 0.0197 | 2.19 | 20000 | 0.3007 | 9.1220 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
