| --- |
| 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 |
| |
