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---
language:
- he
base_model: cantillation/Teamim-AllNusah-whisper-medium_Random-True_Mid
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 [cantillation/Teamim-AllNusah-whisper-medium_Random-True_Mid](https://huggingface.co/cantillation/Teamim-AllNusah-whisper-medium_Random-True_Mid) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1410
- Wer: 11.6348
- Avg Precision Exact: 0.8905
- Avg Recall Exact: 0.8961
- Avg F1 Exact: 0.8928
- Avg Precision Letter Shift: 0.9069
- Avg Recall Letter Shift: 0.9133
- Avg F1 Letter Shift: 0.9096
- Avg Precision Word Level: 0.9091
- Avg Recall Word Level: 0.9146
- Avg F1 Word Level: 0.9114
- Avg Precision Word Shift: 0.9658
- Avg Recall Word Shift: 0.9719
- Avg F1 Word Shift: 0.9683
- Precision Median Exact: 0.9375
- Recall Median Exact: 0.9412
- F1 Median Exact: 0.9630
- 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: 0.6
- Recall Min Word Shift: 0.6429
- F1 Min Word Shift: 0.6429
## 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: 100
- training_steps: 5000
- 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 | 0.0 | 1 | 2.8792 | 88.9597 | 0.1569 | 0.1670 | 0.1602 | 0.1946 | 0.2074 | 0.1987 | 0.2144 | 0.2256 | 0.2176 | 0.3720 | 0.3988 | 0.3809 | 0.1088 | 0.125 | 0.1213 | 0.75 | 0.7692 | 0.7407 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.2893 | 0.04 | 500 | 0.1747 | 17.9830 | 0.8373 | 0.8390 | 0.8373 | 0.8615 | 0.8649 | 0.8624 | 0.8683 | 0.8713 | 0.8691 | 0.9388 | 0.9424 | 0.9398 | 0.9199 | 0.9199 | 0.9016 | 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 |
| 0.209 | 0.08 | 1000 | 0.1427 | 14.5648 | 0.8556 | 0.8629 | 0.8586 | 0.8786 | 0.8862 | 0.8818 | 0.8835 | 0.8907 | 0.8865 | 0.9515 | 0.9579 | 0.9541 | 0.9286 | 0.9286 | 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 | 0.3333 | 0.3077 | 0.32 |
| 0.1831 | 0.12 | 1500 | 0.1381 | 14.2675 | 0.8740 | 0.8825 | 0.8776 | 0.8951 | 0.9045 | 0.8991 | 0.8990 | 0.9074 | 0.9026 | 0.9536 | 0.9605 | 0.9565 | 0.9286 | 0.9310 | 0.9333 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6 | 0.6429 | 0.6207 |
| 0.1371 | 0.16 | 2000 | 0.1376 | 13.5244 | 0.8738 | 0.8789 | 0.8757 | 0.8951 | 0.9004 | 0.8971 | 0.8987 | 0.9032 | 0.9004 | 0.9588 | 0.9627 | 0.9602 | 0.9333 | 0.9333 | 0.9333 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6923 | 0.7143 | 0.7333 |
| 0.1138 | 0.2 | 2500 | 0.1359 | 12.7601 | 0.8774 | 0.8859 | 0.8811 | 0.8963 | 0.9055 | 0.9003 | 0.9003 | 0.9072 | 0.9032 | 0.9582 | 0.9665 | 0.9618 | 0.9333 | 0.9333 | 0.9600 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6 | 0.6923 | 0.6429 |
| 0.1104 | 0.24 | 3000 | 0.1356 | 12.8450 | 0.8749 | 0.8821 | 0.8780 | 0.8912 | 0.8993 | 0.8947 | 0.8940 | 0.9010 | 0.8970 | 0.9582 | 0.9657 | 0.9614 | 0.9333 | 0.9333 | 0.9565 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6 | 0.7273 | 0.6667 |
| 0.0909 | 0.28 | 3500 | 0.1409 | 12.4204 | 0.8808 | 0.8873 | 0.8835 | 0.9007 | 0.9080 | 0.9038 | 0.9031 | 0.9100 | 0.9060 | 0.9639 | 0.9706 | 0.9667 | 0.9333 | 0.9333 | 0.9488 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6 | 0.6923 | 0.6429 |
| 0.09 | 0.32 | 4000 | 0.1370 | 12.0170 | 0.8886 | 0.8910 | 0.8893 | 0.9053 | 0.9085 | 0.9064 | 0.9079 | 0.9106 | 0.9088 | 0.9655 | 0.9685 | 0.9665 | 0.9375 | 0.9393 | 0.9630 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6667 | 0.7333 | 0.7143 |
| 0.0685 | 0.36 | 4500 | 0.1405 | 11.9533 | 0.8912 | 0.8946 | 0.8924 | 0.9079 | 0.9121 | 0.9095 | 0.9103 | 0.9140 | 0.9117 | 0.9650 | 0.9703 | 0.9672 | 0.9412 | 0.9412 | 0.9630 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6 | 0.6429 | 0.6429 |
| 0.0647 | 0.4 | 5000 | 0.1410 | 11.6348 | 0.8905 | 0.8961 | 0.8928 | 0.9069 | 0.9133 | 0.9096 | 0.9091 | 0.9146 | 0.9114 | 0.9658 | 0.9719 | 0.9683 | 0.9375 | 0.9412 | 0.9630 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6 | 0.6429 | 0.6429 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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