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---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Teamim-small_WeightDecay-0.05_Combined-Data_date-17-07-2024_10-08
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. -->
# Teamim-small_WeightDecay-0.05_Combined-Data_date-17-07-2024_10-08
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1496
- Wer: 9.8776
- Avg Precision Exact: 0.9087
- Avg Recall Exact: 0.9107
- Avg F1 Exact: 0.9093
- Avg Precision Letter Shift: 0.9225
- Avg Recall Letter Shift: 0.9246
- Avg F1 Letter Shift: 0.9232
- Avg Precision Word Level: 0.9249
- Avg Recall Word Level: 0.9267
- Avg F1 Word Level: 0.9254
- Avg Precision Word Shift: 0.9697
- Avg Recall Word Shift: 0.9724
- Avg F1 Word Shift: 0.9706
- 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: 0.1
- F1 Min Word Shift: 0.1176
## 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: 200000
- 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 | 7.3549 | 108.0965 | 0.0006 | 0.0037 | 0.0009 | 0.0204 | 0.0217 | 0.0204 | 0.0078 | 0.0760 | 0.0140 | 0.1364 | 0.1552 | 0.1388 | 0.0 | 0.0 | 0.0 | 0.2 | 1.0 | 0.3077 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0527 | 0.5167 | 10000 | 0.1247 | 17.8011 | 0.8390 | 0.8441 | 0.8410 | 0.8596 | 0.8648 | 0.8616 | 0.8632 | 0.8682 | 0.8651 | 0.9344 | 0.9409 | 0.9369 | 0.9167 | 0.9167 | 0.9167 | 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 | 0.0667 | 0.0714 |
| 0.0181 | 1.0334 | 20000 | 0.1178 | 14.7550 | 0.8697 | 0.8697 | 0.8690 | 0.8884 | 0.8887 | 0.8878 | 0.8911 | 0.8918 | 0.8907 | 0.9502 | 0.9517 | 0.9501 | 0.9333 | 0.9333 | 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 |
| 0.0121 | 1.5501 | 30000 | 0.1202 | 13.1219 | 0.8866 | 0.8899 | 0.8878 | 0.9039 | 0.9074 | 0.9052 | 0.9067 | 0.9103 | 0.9080 | 0.9551 | 0.9595 | 0.9568 | 1.0 | 1.0 | 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 | 0.1429 | 0.125 | 0.1333 |
| 0.0041 | 2.0668 | 40000 | 0.1301 | 12.2219 | 0.8897 | 0.8925 | 0.8907 | 0.9058 | 0.9087 | 0.9068 | 0.9089 | 0.9114 | 0.9097 | 0.9579 | 0.9612 | 0.9591 | 1.0 | 1.0 | 0.9655 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.125 | 0.1 | 0.1176 |
| 0.0053 | 2.5834 | 50000 | 0.1294 | 12.3635 | 0.8863 | 0.8873 | 0.8864 | 0.9030 | 0.9042 | 0.9031 | 0.9055 | 0.9068 | 0.9057 | 0.9594 | 0.9623 | 0.9603 | 1.0 | 1.0 | 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 | 0.0909 | 0.1 | 0.0952 |
| 0.01 | 3.1001 | 60000 | 0.1385 | 12.5586 | 0.8824 | 0.8860 | 0.8838 | 0.8984 | 0.9023 | 0.8999 | 0.9010 | 0.9049 | 0.9025 | 0.9557 | 0.9609 | 0.9578 | 1.0 | 1.0 | 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 | 0.1429 | 0.1 | 0.1176 |
| 0.0037 | 3.6168 | 70000 | 0.1374 | 12.4076 | 0.8905 | 0.8936 | 0.8916 | 0.9062 | 0.9093 | 0.9073 | 0.9085 | 0.9115 | 0.9095 | 0.9561 | 0.9598 | 0.9574 | 1.0 | 1.0 | 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 | 0.1429 | 0.1111 | 0.125 |
| 0.0031 | 4.1335 | 80000 | 0.1370 | 11.7562 | 0.8959 | 0.8971 | 0.8961 | 0.9115 | 0.9128 | 0.9117 | 0.9137 | 0.9150 | 0.9139 | 0.9610 | 0.9639 | 0.9620 | 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 | 0.0 | 0.0 | 0.0 |
| 0.0012 | 4.6502 | 90000 | 0.1371 | 11.4573 | 0.8972 | 0.8963 | 0.8964 | 0.9126 | 0.9119 | 0.9118 | 0.9150 | 0.9146 | 0.9144 | 0.9655 | 0.9665 | 0.9655 | 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 | 0.1 | 0.1176 |
| 0.0021 | 5.1669 | 100000 | 0.1448 | 11.6492 | 0.8926 | 0.8952 | 0.8935 | 0.9075 | 0.9103 | 0.9085 | 0.9102 | 0.9128 | 0.9111 | 0.9601 | 0.9637 | 0.9614 | 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 | 0.1 | 0.1053 |
| 0.0013 | 5.6836 | 110000 | 0.1440 | 11.0199 | 0.9003 | 0.9033 | 0.9014 | 0.9156 | 0.9188 | 0.9168 | 0.9179 | 0.9211 | 0.9191 | 0.9645 | 0.9685 | 0.9660 | 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 | 0.1 | 0.1176 |
| 0.0004 | 6.2003 | 120000 | 0.1427 | 10.7901 | 0.9033 | 0.9043 | 0.9035 | 0.9183 | 0.9194 | 0.9185 | 0.9205 | 0.9215 | 0.9206 | 0.9667 | 0.9686 | 0.9672 | 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 | 0.1 | 0.1176 |
| 0.0005 | 6.7170 | 130000 | 0.1434 | 10.7178 | 0.9012 | 0.9039 | 0.9021 | 0.9161 | 0.9189 | 0.9171 | 0.9185 | 0.9212 | 0.9194 | 0.9648 | 0.9684 | 0.9662 | 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 |
| 0.0006 | 7.2336 | 140000 | 0.1452 | 10.7776 | 0.9014 | 0.9041 | 0.9023 | 0.9162 | 0.9190 | 0.9172 | 0.9187 | 0.9211 | 0.9195 | 0.9661 | 0.9699 | 0.9676 | 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 |
| 0.0005 | 7.7503 | 150000 | 0.1471 | 10.6297 | 0.9037 | 0.9039 | 0.9034 | 0.9177 | 0.9180 | 0.9175 | 0.9198 | 0.9199 | 0.9195 | 0.9656 | 0.9673 | 0.9660 | 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 | 0.1 | 0.1176 |
| 0.0001 | 8.2670 | 160000 | 0.1484 | 10.3905 | 0.9035 | 0.9068 | 0.9048 | 0.9177 | 0.9212 | 0.9191 | 0.9200 | 0.9231 | 0.9212 | 0.9667 | 0.9703 | 0.9680 | 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 | 0.1 | 0.1176 |
| 0.0019 | 8.7837 | 170000 | 0.1468 | 10.1136 | 0.9078 | 0.9099 | 0.9085 | 0.9220 | 0.9242 | 0.9227 | 0.9244 | 0.9262 | 0.9249 | 0.9679 | 0.9704 | 0.9687 | 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 | 0.1 | 0.1176 |
| 0.0 | 9.3004 | 180000 | 0.1495 | 10.1167 | 0.9051 | 0.9073 | 0.9058 | 0.9194 | 0.9217 | 0.9202 | 0.9219 | 0.9241 | 0.9226 | 0.9681 | 0.9709 | 0.9691 | 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 | 0.1 | 0.1176 |
| 0.0 | 9.8171 | 190000 | 0.1497 | 10.0412 | 0.9078 | 0.9104 | 0.9087 | 0.9218 | 0.9245 | 0.9227 | 0.9243 | 0.9266 | 0.9251 | 0.9680 | 0.9713 | 0.9692 | 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 | 0.1 | 0.1176 |
| 0.0 | 10.3338 | 200000 | 0.1496 | 9.8776 | 0.9087 | 0.9107 | 0.9093 | 0.9225 | 0.9246 | 0.9232 | 0.9249 | 0.9267 | 0.9254 | 0.9697 | 0.9724 | 0.9706 | 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 | 0.1 | 0.1176 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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