QoLT_largev2_freeze_2
This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0155
- Wer: 12.6984
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 600
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0003 | 49.89 | 100 | 0.0081 | 14.2857 |
| 0.0 | 99.89 | 200 | 0.0040 | 21.6931 |
| 0.0 | 149.89 | 300 | 0.0036 | 21.6931 |
| 0.0 | 199.89 | 400 | 0.0036 | 26.9841 |
| 0.0 | 249.89 | 500 | 0.0037 | 27.5132 |
| 0.0 | 299.89 | 600 | 0.0036 | 30.6878 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.3
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