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amk-whisper
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: 1.1902
- Wer: 40.3587
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 20.0 | 20 | 0.7838 | 30.9417 |
| 0.8511 | 40.0 | 40 | 1.0878 | 44.8430 |
| 0.0794 | 60.0 | 60 | 1.1466 | 39.4619 |
| 0.001 | 80.0 | 80 | 1.1872 | 39.9103 |
| 0.0004 | 100.0 | 100 | 1.1902 | 40.3587 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
