whisper-large-v3-atcosim
This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0573
- Wer: 15.7807
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
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 12500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0031 | 8.33 | 1000 | 0.0372 | 54.8342 |
| 0.0005 | 16.67 | 2000 | 0.0415 | 20.1519 |
| 0.0024 | 25.0 | 3000 | 0.0392 | 10.2102 |
| 0.0 | 33.33 | 4000 | 0.0469 | 18.6609 |
| 0.0 | 41.67 | 5000 | 0.0493 | 17.3180 |
| 0.0 | 50.0 | 6000 | 0.0511 | 16.8179 |
| 0.0 | 58.33 | 7000 | 0.0526 | 16.4753 |
| 0.0 | 66.67 | 8000 | 0.0538 | 16.5725 |
| 0.0 | 75.0 | 9000 | 0.0550 | 15.9983 |
| 0.0 | 83.33 | 10000 | 0.0560 | 15.7205 |
| 0.0 | 91.67 | 11000 | 0.0568 | 15.7159 |
| 0.0 | 100.0 | 12000 | 0.0573 | 15.7807 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.1