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whisper-base-lastversion
This model is a fine-tuned version of openai/whisper-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1732
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000025
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: gpu
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 80000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3116 | 1 | 5000 | 0.6231 |
| 0.2104 | 3 | 10000 | 0.4287 |
| 0.1729 | 4 | 15000 | 0.3421 |
| 0.1472 | 6 | 20000 | 0.3211 |
| 0.128 | 7 | 25000 | 0.2811 |
| 0.1065 | 9 | 30000 | 0.2649 |
| 0.0995 | 10 | 35000 | 0.2523 |
| 0.0812 | 12 | 40000 | 0.2401 |
| 0.066 | 14 | 45000 | 0.2311 |
| 0.0574 | 15 | 50000 | 0.2132 |
| 0.0463 | 17 | 55000 | 0.2077 |
| 0.04 | 18 | 60000 | 0.1957 |
| 0.0314 | 19 | 65000 | 0.1813 |
| 0.0305 | 20 | 70000 | 0.1802 |
| 0.0298 | 21 | 75000 | 0.1755 |
| 0.0265 | 22 | 80000 | 0.1732 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0a0+gitcc01568
- Datasets 2.13.1
- Tokenizers 0.13.3
