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---
library_name: transformers
language:
- he
license: mit
base_model: openai/whisper-Large-v3-Turbo
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: he-cantillation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# he-cantillation
This model is a fine-tuned version of [openai/whisper-Large-v3-Turbo](https://huggingface.co/openai/whisper-Large-v3-Turbo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4731
- Wer: 37.9958
- Avg Precision Exact: 0.4726
- Avg Recall Exact: 0.4821
- Avg F1 Exact: 0.4762
- Avg Precision Letter Shift: 0.4946
- Avg Recall Letter Shift: 0.5062
- Avg F1 Letter Shift: 0.4989
- Avg Precision Word Level: 0.5073
- Avg Recall Word Level: 0.5201
- Avg F1 Word Level: 0.5116
- Avg Precision Word Shift: 0.6840
- Avg Recall Word Shift: 0.7091
- Avg F1 Word Shift: 0.6925
- Precision Median Exact: 0.35
- Recall Median Exact: 0.3709
- F1 Median Exact: 0.3577
- 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: 0.0
- Recall Min Word Shift: 0.0
- F1 Min Word Shift: 0.0
## 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: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 80000
- 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|
| No log | 0.0001 | 1 | 6.2394 | 110.8685 | 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 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 0.1211 | 0.2962 | 2500 | 0.7739 | 60.3892 | 0.2896 | 0.3023 | 0.2948 | 0.3230 | 0.3381 | 0.3289 | 0.3400 | 0.3560 | 0.3460 | 0.5354 | 0.5766 | 0.5518 | 0.1538 | 0.1732 | 0.1611 | 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 |
| 0.0734 | 0.5925 | 5000 | 0.6116 | 52.4895 | 0.3503 | 0.3636 | 0.3556 | 0.3787 | 0.3954 | 0.3854 | 0.3946 | 0.4131 | 0.4020 | 0.5867 | 0.6260 | 0.6026 | 0.2 | 0.2188 | 0.2064 | 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 |
| 0.0701 | 0.8887 | 7500 | 0.5577 | 49.6667 | 0.3772 | 0.3910 | 0.3826 | 0.4071 | 0.4245 | 0.4137 | 0.4220 | 0.4407 | 0.4287 | 0.6095 | 0.6498 | 0.6242 | 0.2294 | 0.25 | 0.2371 | 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 |
| 0.0455 | 1.1850 | 10000 | 0.5711 | 49.5251 | 0.3459 | 0.3571 | 0.3502 | 0.3737 | 0.3881 | 0.3792 | 0.3910 | 0.4083 | 0.3973 | 0.5849 | 0.6229 | 0.5993 | 0.1786 | 0.2 | 0.1885 | 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 |
| 0.0426 | 1.4812 | 12500 | 0.5363 | 47.2362 | 0.3891 | 0.3960 | 0.3916 | 0.4174 | 0.4267 | 0.4208 | 0.4314 | 0.4450 | 0.4364 | 0.6261 | 0.6527 | 0.6360 | 0.2343 | 0.25 | 0.2407 | 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 |
| 0.0639 | 1.7775 | 15000 | 0.5074 | 45.9014 | 0.4086 | 0.4159 | 0.4110 | 0.4382 | 0.4475 | 0.4411 | 0.4531 | 0.4635 | 0.4563 | 0.6393 | 0.6673 | 0.6493 | 0.25 | 0.2710 | 0.2568 | 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 |
| 0.0197 | 2.0737 | 17500 | 0.5560 | 47.4725 | 0.3848 | 0.3992 | 0.3897 | 0.4105 | 0.4288 | 0.4169 | 0.4245 | 0.4459 | 0.4320 | 0.6001 | 0.6449 | 0.6157 | 0.2045 | 0.2353 | 0.2171 | 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 |
| 0.0185 | 2.3699 | 20000 | 0.5010 | 45.9830 | 0.4033 | 0.4160 | 0.4083 | 0.4301 | 0.4455 | 0.4360 | 0.4452 | 0.4636 | 0.4517 | 0.6296 | 0.6678 | 0.6443 | 0.2347 | 0.2632 | 0.2455 | 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 |
| 0.035 | 2.6662 | 22500 | 0.4793 | 44.1070 | 0.4046 | 0.4156 | 0.4085 | 0.4318 | 0.4452 | 0.4367 | 0.4469 | 0.4620 | 0.4518 | 0.6434 | 0.6753 | 0.6545 | 0.2630 | 0.2873 | 0.2692 | 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 |
| 0.0125 | 2.9624 | 25000 | 0.4838 | 43.4315 | 0.4177 | 0.4315 | 0.4231 | 0.4443 | 0.4613 | 0.4509 | 0.4589 | 0.4782 | 0.4661 | 0.6398 | 0.6802 | 0.6556 | 0.2650 | 0.2948 | 0.2748 | 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 |
| 0.0141 | 3.2587 | 27500 | 0.4849 | 42.0296 | 0.4273 | 0.4390 | 0.4319 | 0.4507 | 0.4649 | 0.4562 | 0.4636 | 0.4790 | 0.4694 | 0.6480 | 0.6827 | 0.6611 | 0.2857 | 0.3150 | 0.3014 | 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 |
| 0.0458 | 3.5549 | 30000 | 0.4973 | 45.4608 | 0.4103 | 0.4230 | 0.4150 | 0.4362 | 0.4511 | 0.4413 | 0.4489 | 0.4653 | 0.4545 | 0.6252 | 0.6608 | 0.6377 | 0.2411 | 0.2620 | 0.2483 | 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 |
| 0.0229 | 3.8512 | 32500 | 0.4870 | 43.0668 | 0.4337 | 0.4459 | 0.4384 | 0.4585 | 0.4739 | 0.4644 | 0.4721 | 0.4896 | 0.4786 | 0.6519 | 0.6883 | 0.6656 | 0.2903 | 0.3158 | 0.2987 | 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 |
| 0.0151 | 4.1474 | 35000 | 0.4685 | 41.9114 | 0.4322 | 0.4410 | 0.4351 | 0.4558 | 0.4669 | 0.4596 | 0.4700 | 0.4830 | 0.4743 | 0.6506 | 0.6798 | 0.6608 | 0.2667 | 0.2910 | 0.2798 | 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 |
| 0.0175 | 4.4437 | 37500 | 0.5112 | 42.8421 | 0.4149 | 0.4269 | 0.4190 | 0.4405 | 0.4558 | 0.4459 | 0.4547 | 0.4715 | 0.4601 | 0.6293 | 0.6670 | 0.6422 | 0.2411 | 0.2667 | 0.2516 | 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 |
| 0.0097 | 4.7399 | 40000 | 0.4808 | 42.9647 | 0.4254 | 0.4353 | 0.4290 | 0.4481 | 0.4613 | 0.4531 | 0.4618 | 0.4777 | 0.4676 | 0.6326 | 0.6680 | 0.6452 | 0.2407 | 0.2639 | 0.25 | 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 |
| 0.0072 | 5.0361 | 42500 | 0.4667 | 40.1506 | 0.4493 | 0.4608 | 0.4538 | 0.4747 | 0.4878 | 0.4793 | 0.4891 | 0.5041 | 0.4940 | 0.6741 | 0.7059 | 0.6854 | 0.3 | 0.3333 | 0.3171 | 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 |
| 0.0092 | 5.3324 | 45000 | 0.4639 | 40.5386 | 0.4385 | 0.4534 | 0.4446 | 0.4630 | 0.4804 | 0.4697 | 0.4766 | 0.4966 | 0.4840 | 0.6494 | 0.6895 | 0.6645 | 0.2903 | 0.3247 | 0.3041 | 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 |
| 0.0098 | 5.6286 | 47500 | 0.5158 | 43.3221 | 0.4220 | 0.4344 | 0.4268 | 0.4456 | 0.4628 | 0.4519 | 0.4580 | 0.4782 | 0.4651 | 0.6228 | 0.6661 | 0.6383 | 0.2474 | 0.2784 | 0.2584 | 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 |
| 0.0043 | 5.9249 | 50000 | 0.4820 | 41.5204 | 0.4424 | 0.4514 | 0.4455 | 0.4657 | 0.4765 | 0.4695 | 0.4774 | 0.4899 | 0.4819 | 0.6544 | 0.6795 | 0.6632 | 0.2941 | 0.3207 | 0.3000 | 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 |
| 0.0043 | 6.2211 | 52500 | 0.4708 | 41.5802 | 0.4544 | 0.4684 | 0.4588 | 0.4779 | 0.4944 | 0.4832 | 0.4905 | 0.5092 | 0.4964 | 0.6580 | 0.6914 | 0.6685 | 0.3243 | 0.3571 | 0.3333 | 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 |
| 0.0105 | 6.5174 | 55000 | 0.5031 | 42.6977 | 0.4399 | 0.4539 | 0.4452 | 0.4629 | 0.4799 | 0.4693 | 0.4762 | 0.4950 | 0.4830 | 0.6501 | 0.6888 | 0.6642 | 0.3037 | 0.3422 | 0.32 | 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 |
| 0.0161 | 6.8136 | 57500 | 0.4820 | 39.5101 | 0.4516 | 0.4628 | 0.4556 | 0.4728 | 0.4866 | 0.4776 | 0.4855 | 0.5023 | 0.4910 | 0.6528 | 0.6893 | 0.6656 | 0.3118 | 0.3448 | 0.3231 | 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 |
| 0.0069 | 7.1098 | 60000 | 0.4743 | 39.6487 | 0.4535 | 0.4651 | 0.4577 | 0.4759 | 0.4897 | 0.4807 | 0.4889 | 0.5049 | 0.4944 | 0.6699 | 0.7018 | 0.6815 | 0.3333 | 0.3507 | 0.3333 | 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 |
| 0.0098 | 7.4061 | 62500 | 0.4884 | 40.9938 | 0.4399 | 0.4524 | 0.4447 | 0.4623 | 0.4774 | 0.4677 | 0.4744 | 0.4932 | 0.4806 | 0.6489 | 0.6895 | 0.6630 | 0.2896 | 0.32 | 0.3038 | 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 |
| 0.0012 | 7.7023 | 65000 | 0.4967 | 40.0236 | 0.4449 | 0.4579 | 0.4498 | 0.4664 | 0.4817 | 0.4720 | 0.4785 | 0.4957 | 0.4844 | 0.6567 | 0.6922 | 0.6690 | 0.3086 | 0.3409 | 0.3205 | 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 |
| 0.0025 | 7.9986 | 67500 | 0.4861 | 39.8325 | 0.4618 | 0.4724 | 0.4657 | 0.4854 | 0.4981 | 0.4902 | 0.4981 | 0.5122 | 0.5031 | 0.6722 | 0.7016 | 0.6828 | 0.3459 | 0.375 | 0.3529 | 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 |
| 0.0018 | 8.2948 | 70000 | 0.4827 | 39.1016 | 0.4563 | 0.4680 | 0.4607 | 0.4785 | 0.4923 | 0.4836 | 0.4907 | 0.5061 | 0.4961 | 0.6707 | 0.7028 | 0.6821 | 0.3333 | 0.3624 | 0.3485 | 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 |
| 0.0027 | 8.5911 | 72500 | 0.4808 | 38.9368 | 0.4607 | 0.4718 | 0.4647 | 0.4825 | 0.4965 | 0.4875 | 0.4955 | 0.5120 | 0.5010 | 0.6703 | 0.7022 | 0.6813 | 0.3333 | 0.3709 | 0.3521 | 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 |
| 0.0018 | 8.8873 | 75000 | 0.4793 | 38.9076 | 0.4630 | 0.4733 | 0.4669 | 0.4852 | 0.4984 | 0.4901 | 0.4975 | 0.5123 | 0.5025 | 0.6681 | 0.6997 | 0.6789 | 0.3438 | 0.3718 | 0.3519 | 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 |
| 0.0015 | 9.1836 | 77500 | 0.4782 | 38.2453 | 0.4716 | 0.4820 | 0.4755 | 0.4931 | 0.5057 | 0.4978 | 0.5052 | 0.5191 | 0.5100 | 0.6811 | 0.7099 | 0.6913 | 0.3667 | 0.3908 | 0.3774 | 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 |
| 0.001 | 9.4798 | 80000 | 0.4731 | 37.9958 | 0.4726 | 0.4821 | 0.4762 | 0.4946 | 0.5062 | 0.4989 | 0.5073 | 0.5201 | 0.5116 | 0.6840 | 0.7091 | 0.6925 | 0.35 | 0.3709 | 0.3577 | 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 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu126
- Datasets 2.12.0
- Tokenizers 0.20.1
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