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---
base_model: openai/whisper-medium
datasets:
- generator
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: whisper-medium-sb-lug-eng
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. -->
# whisper-medium-sb-lug-eng
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1720
- Wer Lug: 0.81
- Wer Eng: 0.068
- Wer Mean: 0.439
- Cer Lug: 0.494
- Cer Eng: 0.039
- Cer Mean: 0.267
## 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: 8
- 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: 30000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Lug | Wer Eng | Wer Mean | Cer Lug | Cer Eng | Cer Mean |
|:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|:--------:|:-------:|:-------:|:--------:|
| 0.9804 | 0.0167 | 500 | 0.3683 | 0.692 | 0.043 | 0.368 | 0.203 | 0.019 | 0.111 |
| 0.7775 | 0.0333 | 1000 | 0.2594 | 0.725 | 0.044 | 0.385 | 0.395 | 0.019 | 0.207 |
| 0.6492 | 0.05 | 1500 | 0.2316 | 0.649 | 0.041 | 0.345 | 0.263 | 0.02 | 0.142 |
| 0.6128 | 0.0667 | 2000 | 0.2111 | 0.513 | 0.04 | 0.277 | 0.197 | 0.018 | 0.108 |
| 0.543 | 0.0833 | 2500 | 0.2023 | 0.579 | 0.043 | 0.311 | 0.239 | 0.018 | 0.129 |
| 0.5461 | 0.1 | 3000 | 0.1932 | 0.425 | 0.04 | 0.233 | 0.138 | 0.019 | 0.078 |
| 0.5545 | 0.1167 | 3500 | 0.1836 | 0.624 | 0.043 | 0.334 | 0.381 | 0.021 | 0.201 |
| 0.4895 | 0.1333 | 4000 | 0.1802 | 0.407 | 0.043 | 0.225 | 0.156 | 0.022 | 0.089 |
| 0.4922 | 0.15 | 4500 | 0.1771 | 0.377 | 0.051 | 0.214 | 0.136 | 0.033 | 0.084 |
| 0.521 | 0.1667 | 5000 | 0.1817 | 0.316 | 0.049 | 0.183 | 0.097 | 0.028 | 0.062 |
| 0.3948 | 1.0153 | 5500 | 0.1724 | 0.422 | 0.079 | 0.251 | 0.17 | 0.057 | 0.113 |
| 0.3914 | 1.032 | 6000 | 0.1727 | 0.744 | 0.04 | 0.392 | 0.651 | 0.018 | 0.334 |
| 0.3807 | 1.0487 | 6500 | 0.1730 | 0.585 | 0.053 | 0.319 | 0.428 | 0.028 | 0.228 |
| 0.395 | 1.0653 | 7000 | 0.1701 | 0.737 | 0.043 | 0.39 | 0.635 | 0.024 | 0.329 |
| 0.3774 | 1.082 | 7500 | 0.1654 | 0.545 | 0.046 | 0.296 | 0.396 | 0.024 | 0.21 |
| 0.4017 | 1.0987 | 8000 | 0.1626 | 0.465 | 0.046 | 0.256 | 0.28 | 0.024 | 0.152 |
| 0.3901 | 1.1153 | 8500 | 0.1593 | 0.516 | 0.051 | 0.283 | 0.25 | 0.026 | 0.138 |
| 0.3829 | 1.1320 | 9000 | 0.1608 | 0.48 | 0.049 | 0.264 | 0.247 | 0.024 | 0.135 |
| 0.3536 | 1.1487 | 9500 | 0.1657 | 0.37 | 0.043 | 0.207 | 0.143 | 0.021 | 0.082 |
| 0.3506 | 1.1653 | 10000 | 0.1606 | 0.395 | 0.041 | 0.218 | 0.172 | 0.021 | 0.097 |
| 0.2737 | 2.014 | 10500 | 0.1604 | 0.457 | 0.07 | 0.263 | 0.235 | 0.044 | 0.139 |
| 0.3073 | 2.0307 | 11000 | 0.1626 | 0.458 | 0.046 | 0.252 | 0.243 | 0.022 | 0.132 |
| 0.2906 | 2.0473 | 11500 | 0.1581 | 0.444 | 0.062 | 0.253 | 0.222 | 0.038 | 0.13 |
| 0.2882 | 2.064 | 12000 | 0.1591 | 0.519 | 0.053 | 0.286 | 0.3 | 0.024 | 0.162 |
| 0.2642 | 2.0807 | 12500 | 0.1630 | 0.547 | 0.05 | 0.299 | 0.293 | 0.029 | 0.161 |
| 0.2848 | 2.0973 | 13000 | 0.1627 | 0.509 | 0.055 | 0.282 | 0.244 | 0.03 | 0.137 |
| 0.2887 | 2.114 | 13500 | 0.1585 | 0.524 | 0.067 | 0.296 | 0.28 | 0.047 | 0.163 |
| 0.2879 | 2.1307 | 14000 | 0.1593 | 0.646 | 0.065 | 0.356 | 0.355 | 0.045 | 0.2 |
| 0.2955 | 2.1473 | 14500 | 0.1581 | 0.873 | 0.062 | 0.468 | 0.512 | 0.038 | 0.275 |
| 0.2639 | 2.164 | 15000 | 0.1533 | 0.772 | 0.057 | 0.414 | 0.454 | 0.037 | 0.245 |
| 0.2111 | 3.0127 | 15500 | 0.1622 | 0.776 | 0.074 | 0.425 | 0.518 | 0.046 | 0.282 |
| 0.2299 | 3.0293 | 16000 | 0.1628 | 0.849 | 0.061 | 0.455 | 0.559 | 0.036 | 0.297 |
| 0.2279 | 3.046 | 16500 | 0.1633 | 0.803 | 0.064 | 0.434 | 0.632 | 0.036 | 0.334 |
| 0.2339 | 3.0627 | 17000 | 0.1617 | 0.845 | 0.045 | 0.445 | 0.553 | 0.022 | 0.288 |
| 0.2387 | 3.0793 | 17500 | 0.1599 | 0.773 | 0.055 | 0.414 | 0.436 | 0.029 | 0.232 |
| 0.2098 | 3.096 | 18000 | 0.1616 | 0.675 | 0.059 | 0.367 | 0.45 | 0.037 | 0.243 |
| 0.2201 | 3.1127 | 18500 | 0.1619 | 0.713 | 0.066 | 0.389 | 0.476 | 0.039 | 0.257 |
| 0.2312 | 3.1293 | 19000 | 0.1603 | 0.994 | 0.053 | 0.524 | 0.605 | 0.03 | 0.318 |
| 0.2389 | 3.146 | 19500 | 0.1572 | 0.751 | 0.054 | 0.403 | 0.455 | 0.032 | 0.244 |
| 0.2183 | 3.1627 | 20000 | 0.1635 | 0.667 | 0.056 | 0.362 | 0.42 | 0.034 | 0.227 |
| 0.1707 | 4.0113 | 20500 | 0.1654 | 0.682 | 0.05 | 0.366 | 0.433 | 0.026 | 0.23 |
| 0.1874 | 4.028 | 21000 | 0.1641 | 0.744 | 0.054 | 0.399 | 0.425 | 0.03 | 0.228 |
| 0.1836 | 4.0447 | 21500 | 0.1666 | 0.651 | 0.063 | 0.357 | 0.397 | 0.039 | 0.218 |
| 0.1847 | 4.0613 | 22000 | 0.1635 | 0.788 | 0.069 | 0.429 | 0.502 | 0.044 | 0.273 |
| 0.1742 | 4.078 | 22500 | 0.1651 | 0.695 | 0.051 | 0.373 | 0.4 | 0.027 | 0.214 |
| 0.1733 | 4.0947 | 23000 | 0.1652 | 0.678 | 0.064 | 0.371 | 0.427 | 0.039 | 0.233 |
| 0.1651 | 4.1113 | 23500 | 0.1659 | 0.666 | 0.071 | 0.369 | 0.458 | 0.046 | 0.252 |
| 0.1924 | 4.128 | 24000 | 0.1664 | 0.792 | 0.069 | 0.431 | 0.486 | 0.046 | 0.266 |
| 0.1828 | 4.1447 | 24500 | 0.1670 | 0.746 | 0.068 | 0.407 | 0.538 | 0.043 | 0.291 |
| 0.165 | 4.1613 | 25000 | 0.1675 | 0.746 | 0.072 | 0.409 | 0.469 | 0.047 | 0.258 |
| 0.1437 | 5.01 | 25500 | 0.1706 | 0.728 | 0.066 | 0.397 | 0.481 | 0.04 | 0.261 |
| 0.148 | 5.0267 | 26000 | 0.1700 | 0.755 | 0.069 | 0.412 | 0.457 | 0.041 | 0.249 |
| 0.1509 | 5.0433 | 26500 | 0.1700 | 0.787 | 0.068 | 0.427 | 0.497 | 0.039 | 0.268 |
| 0.1442 | 5.06 | 27000 | 0.1715 | 0.762 | 0.068 | 0.415 | 0.47 | 0.039 | 0.254 |
| 0.1282 | 5.0767 | 27500 | 0.1698 | 0.796 | 0.064 | 0.43 | 0.477 | 0.037 | 0.257 |
| 0.1377 | 5.0933 | 28000 | 0.1710 | 0.796 | 0.068 | 0.432 | 0.481 | 0.04 | 0.261 |
| 0.1456 | 5.11 | 28500 | 0.1719 | 0.758 | 0.07 | 0.414 | 0.481 | 0.04 | 0.26 |
| 0.143 | 5.1267 | 29000 | 0.1716 | 0.795 | 0.07 | 0.433 | 0.488 | 0.04 | 0.264 |
| 0.1484 | 5.1433 | 29500 | 0.1719 | 0.812 | 0.069 | 0.44 | 0.492 | 0.04 | 0.266 |
| 0.1463 | 5.16 | 30000 | 0.1720 | 0.81 | 0.068 | 0.439 | 0.494 | 0.039 | 0.267 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.19.1
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