he-cantillation
This model is a fine-tuned version of ivrit-ai/whisper-v2-pd1-e1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1078
- Wer: 15.5291
- Avg Precision Exact: 0.8760
- Avg Recall Exact: 0.8758
- Avg F1 Exact: 0.8753
- Avg Precision Letter Shift: 0.8960
- Avg Recall Letter Shift: 0.8959
- Avg F1 Letter Shift: 0.8953
- Avg Precision Word Level: 0.8987
- Avg Recall Word Level: 0.8993
- Avg F1 Word Level: 0.8984
- Avg Precision Word Shift: 0.9450
- Avg Recall Word Shift: 0.9466
- Avg F1 Word Shift: 0.9451
- Precision Median Exact: 0.9231
- Recall Median Exact: 0.9231
- F1 Median Exact: 0.9286
- Precision Max Exact: 1.0
- Recall Max Exact: 1.0
- F1 Max Exact: 1.0
- Precision Min Exact: 0.0
- Recall Min Exact: 0.0
- F1 Min Exact: 0.0
- Precision Min Letter Shift: 0.0
- Recall Min Letter Shift: 0.0
- F1 Min Letter Shift: 0.0
- Precision Min Word Level: 0.0
- Recall Min Word Level: 0.0
- F1 Min Word Level: 0.0
- Precision Min Word Shift: 0.0769
- Recall Min Word Shift: 0.0909
- F1 Min Word Shift: 0.0833
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: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Avg Precision Exact | Avg Recall Exact | Avg F1 Exact | Avg Precision Letter Shift | Avg Recall Letter Shift | Avg F1 Letter Shift | Avg Precision Word Level | Avg Recall Word Level | Avg F1 Word Level | Avg Precision Word Shift | Avg Recall Word Shift | Avg F1 Word Shift | Precision Median Exact | Recall Median Exact | F1 Median Exact | Precision Max Exact | Recall Max Exact | F1 Max Exact | Precision Min Exact | Recall Min Exact | F1 Min Exact | Precision Min Letter Shift | Recall Min Letter Shift | F1 Min Letter Shift | Precision Min Word Level | Recall Min Word Level | F1 Min Word Level | Precision Min Word Shift | Recall Min Word Shift | F1 Min Word Shift |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1518 | 0.0517 | 1000 | 0.1823 | 26.0329 | 0.7790 | 0.7842 | 0.7807 | 0.8094 | 0.8153 | 0.8114 | 0.8144 | 0.8207 | 0.8167 | 0.8953 | 0.9056 | 0.8994 | 0.8462 | 0.8571 | 0.8571 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0993 | 0.1033 | 2000 | 0.1316 | 18.8804 | 0.8477 | 0.8540 | 0.8501 | 0.8691 | 0.8757 | 0.8716 | 0.8734 | 0.8799 | 0.8759 | 0.9264 | 0.9342 | 0.9294 | 0.9091 | 0.9167 | 0.9091 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0769 | 0.0909 | 0.0833 |
| 0.0625 | 0.1550 | 3000 | 0.1078 | 15.5291 | 0.8760 | 0.8758 | 0.8753 | 0.8960 | 0.8959 | 0.8953 | 0.8987 | 0.8993 | 0.8984 | 0.9450 | 0.9466 | 0.9451 | 0.9231 | 0.9231 | 0.9286 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0769 | 0.0909 | 0.0833 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.20.0
- Tokenizers 0.19.1