whisper-medium-production
This model is a fine-tuned version of openai/whisper-medium on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3373
- Wer Ortho: 34.2105
- Wer: 34.2105
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 30
- training_steps: 300
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0296 | 20.0 | 60 | 0.3520 | 50.0 | 47.3684 |
| 0.0001 | 40.0 | 120 | 0.3399 | 34.2105 | 34.2105 |
| 0.0 | 60.0 | 180 | 0.3378 | 34.2105 | 34.2105 |
| 0.0 | 80.0 | 240 | 0.3370 | 34.2105 | 34.2105 |
| 0.0 | 100.0 | 300 | 0.3373 | 34.2105 | 34.2105 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Base model
openai/whisper-medium