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---
language:
- he
base_model: ivrit-ai/whisper-v2-pd1-e1
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 [ivrit-ai/whisper-v2-pd1-e1](https://huggingface.co/ivrit-ai/whisper-v2-pd1-e1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0952
- Wer: 7.8511
- Avg Precision Exact: 0.9335
- Avg Recall Exact: 0.9352
- Avg F1 Exact: 0.9340
- Avg Precision Letter Shift: 0.9440
- Avg Recall Letter Shift: 0.9458
- Avg F1 Letter Shift: 0.9446
- Avg Precision Word Level: 0.9462
- Avg Recall Word Level: 0.9479
- Avg F1 Word Level: 0.9467
- Avg Precision Word Shift: 0.9714
- Avg Recall Word Shift: 0.9736
- Avg F1 Word Shift: 0.9721
- Precision Median Exact: 1.0
- Recall Median Exact: 1.0
- F1 Median Exact: 1.0
- 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: 0.1429
- Recall Min Word Shift: 0.125
- F1 Min Word Shift: 0.1333
## 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: 500
- 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 | 5.0835 | 121.5079 | 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 |
| 0.0423 | 0.5167 | 10000 | 0.1010 | 13.7858 | 0.8705 | 0.8797 | 0.8745 | 0.8866 | 0.8961 | 0.8908 | 0.8899 | 0.8991 | 0.8939 | 0.9426 | 0.9519 | 0.9466 | 0.9286 | 0.9412 | 0.9474 | 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 |
| 0.0139 | 1.0334 | 20000 | 0.0950 | 10.6832 | 0.9090 | 0.9076 | 0.9079 | 0.9219 | 0.9205 | 0.9208 | 0.9251 | 0.9237 | 0.9239 | 0.9610 | 0.9611 | 0.9605 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 |
| 0.0089 | 1.5501 | 30000 | 0.0914 | 10.2458 | 0.9091 | 0.9077 | 0.9081 | 0.9208 | 0.9196 | 0.9198 | 0.9231 | 0.9220 | 0.9222 | 0.9596 | 0.9590 | 0.9589 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.125 | 0.1111 |
| 0.0032 | 2.0668 | 40000 | 0.0922 | 9.3269 | 0.9163 | 0.9159 | 0.9157 | 0.9282 | 0.9279 | 0.9277 | 0.9307 | 0.9303 | 0.9301 | 0.9666 | 0.9676 | 0.9667 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0909 | 0.1111 | 0.1176 |
| 0.0025 | 2.5834 | 50000 | 0.0924 | 9.0500 | 0.9171 | 0.9179 | 0.9172 | 0.9283 | 0.9292 | 0.9284 | 0.9307 | 0.9314 | 0.9307 | 0.9656 | 0.9669 | 0.9659 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0022 | 3.1001 | 60000 | 0.0933 | 8.3137 | 0.9272 | 0.9266 | 0.9266 | 0.9377 | 0.9371 | 0.9371 | 0.9399 | 0.9393 | 0.9393 | 0.9702 | 0.9702 | 0.9698 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0006 | 3.6168 | 70000 | 0.0947 | 8.0682 | 0.9287 | 0.9302 | 0.9291 | 0.9393 | 0.9409 | 0.9398 | 0.9417 | 0.9430 | 0.9420 | 0.9706 | 0.9723 | 0.9710 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0002 | 4.1335 | 80000 | 0.0952 | 7.8511 | 0.9335 | 0.9352 | 0.9340 | 0.9440 | 0.9458 | 0.9446 | 0.9462 | 0.9479 | 0.9467 | 0.9714 | 0.9736 | 0.9721 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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