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---
base_model: Lakoc/DeCRED_small_cv_2
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: DeCRED_small_cv_v2_scalar_mixing
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. -->
# DeCRED_small_cv_v2_scalar_mixing
This model is a fine-tuned version of [Lakoc/DeCRED_small_cv_2](https://huggingface.co/Lakoc/DeCRED_small_cv_2) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0838
- Cer: 0.4056
- Wer: 0.6611
- Mer: 0.5983
- Wil: 0.8066
- Wip: 0.1934
- Hits: 20649
- Substitutions: 21863
- Deletions: 4009
- Insertions: 4882
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|
| 6.9657 | 0.98 | 22 | 6.8485 | 59.9990 | 50.8263 | 0.9996 | 1.0000 | 0.0000 | 909 | 45605 | 7 | 2318877 |
| 6.585 | 2.0 | 45 | 6.6096 | 60.0578 | 50.5684 | 0.9996 | 1.0000 | 0.0000 | 990 | 45524 | 7 | 2306961 |
| 6.479 | 2.98 | 67 | 6.3875 | 59.6307 | 50.1500 | 0.9996 | 1.0000 | 0.0000 | 1046 | 45464 | 11 | 2287554 |
| 6.1764 | 4.0 | 90 | 6.1618 | 58.9125 | 49.4174 | 0.9995 | 1.0000 | 0.0000 | 1100 | 45410 | 11 | 2253527 |
| 6.0943 | 4.98 | 112 | 5.9524 | 57.6932 | 48.3373 | 0.9995 | 1.0000 | 0.0000 | 1191 | 45307 | 23 | 2203369 |
| 5.7875 | 6.0 | 135 | 5.7400 | 56.4049 | 47.1201 | 0.9994 | 1.0000 | 0.0000 | 1350 | 45145 | 26 | 2146902 |
| 5.7306 | 6.98 | 157 | 5.5433 | 55.1239 | 46.0142 | 0.9993 | 1.0000 | 0.0000 | 1449 | 45043 | 29 | 2095554 |
| 5.3893 | 8.0 | 180 | 5.3443 | 53.1765 | 44.2142 | 0.9992 | 1.0000 | 0.0000 | 1609 | 44876 | 36 | 2011978 |
| 5.355 | 8.98 | 202 | 5.1604 | 50.8395 | 42.2829 | 0.9991 | 1.0000 | 0.0000 | 1726 | 44743 | 52 | 1922250 |
| 5.0009 | 10.0 | 225 | 4.9748 | 47.9798 | 39.7937 | 0.9990 | 1.0000 | 0.0000 | 1921 | 44525 | 75 | 1806641 |
| 4.9572 | 10.98 | 247 | 4.8037 | 44.5128 | 36.9002 | 0.9988 | 0.9999 | 0.0001 | 2039 | 44377 | 105 | 1672151 |
| 4.7249 | 12.0 | 270 | 4.6314 | 40.9512 | 34.0044 | 0.9985 | 0.9999 | 0.0001 | 2306 | 44103 | 112 | 1537704 |
| 4.5906 | 12.98 | 292 | 4.4729 | 36.5890 | 30.3183 | 0.9982 | 0.9999 | 0.0001 | 2542 | 43829 | 150 | 1366459 |
| 4.3943 | 14.0 | 315 | 4.3138 | 32.2323 | 26.6965 | 0.9978 | 0.9999 | 0.0001 | 2765 | 43562 | 194 | 1198191 |
| 4.3164 | 14.98 | 337 | 4.1678 | 28.0206 | 23.2171 | 0.9972 | 0.9998 | 0.0002 | 3042 | 43206 | 273 | 1036603 |
| 4.1206 | 16.0 | 360 | 4.0216 | 23.6627 | 19.7857 | 0.9964 | 0.9997 | 0.0003 | 3295 | 42841 | 385 | 877224 |
| 4.0375 | 16.98 | 382 | 3.8879 | 19.1286 | 16.0039 | 0.9952 | 0.9996 | 0.0004 | 3622 | 42341 | 558 | 701619 |
| 3.8357 | 18.0 | 405 | 3.7545 | 15.4344 | 12.9968 | 0.9934 | 0.9994 | 0.0006 | 3991 | 41883 | 647 | 562094 |
| 3.7535 | 18.98 | 427 | 3.6327 | 11.6498 | 9.9882 | 0.9907 | 0.9991 | 0.0009 | 4356 | 41365 | 800 | 422498 |
| 3.5453 | 20.0 | 450 | 3.5116 | 9.0630 | 7.8157 | 0.9872 | 0.9987 | 0.0013 | 4705 | 40852 | 964 | 321778 |
| 3.5829 | 20.98 | 472 | 3.4015 | 6.9251 | 6.0536 | 0.9817 | 0.9979 | 0.0021 | 5240 | 40091 | 1190 | 240338 |
| 3.3805 | 22.0 | 495 | 3.2922 | 5.3260 | 4.7628 | 0.9754 | 0.9970 | 0.0030 | 5588 | 39576 | 1357 | 180635 |
| 3.3505 | 22.98 | 517 | 3.1933 | 3.7440 | 3.4844 | 0.9640 | 0.9953 | 0.0047 | 6051 | 38927 | 1543 | 121630 |
| 3.1605 | 24.0 | 540 | 3.0954 | 2.8455 | 2.7410 | 0.9505 | 0.9928 | 0.0072 | 6644 | 38101 | 1776 | 87636 |
| 3.1195 | 24.98 | 562 | 3.0071 | 1.8640 | 1.9752 | 0.9279 | 0.9887 | 0.0113 | 7141 | 37306 | 2074 | 52510 |
| 3.0494 | 26.0 | 585 | 2.9201 | 1.7379 | 1.8401 | 0.9164 | 0.9856 | 0.0144 | 7812 | 36313 | 2396 | 46896 |
| 3.0038 | 26.98 | 607 | 2.8418 | 1.3510 | 1.5287 | 0.8936 | 0.9800 | 0.0200 | 8466 | 35489 | 2566 | 33062 |
| 2.8702 | 28.0 | 630 | 2.7651 | 1.0949 | 1.3176 | 0.8699 | 0.9733 | 0.0267 | 9170 | 34524 | 2827 | 23947 |
| 2.8388 | 28.98 | 652 | 2.6963 | 0.9275 | 1.1758 | 0.8480 | 0.9664 | 0.0336 | 9802 | 33665 | 3054 | 17981 |
| 2.7353 | 30.0 | 675 | 2.6292 | 0.7931 | 1.0642 | 0.8230 | 0.9572 | 0.0428 | 10649 | 32622 | 3250 | 13637 |
| 2.694 | 30.98 | 697 | 2.5693 | 0.7406 | 1.0138 | 0.8049 | 0.9491 | 0.0509 | 11429 | 31693 | 3399 | 12071 |
| 2.63 | 32.0 | 720 | 2.5112 | 0.6716 | 0.9492 | 0.7835 | 0.9395 | 0.0605 | 12203 | 30845 | 3473 | 9839 |
| 2.5981 | 32.98 | 742 | 2.4596 | 0.6256 | 0.9070 | 0.7648 | 0.9298 | 0.0702 | 12979 | 29960 | 3582 | 8653 |
| 2.521 | 34.0 | 765 | 2.4099 | 0.5947 | 0.8741 | 0.7464 | 0.9192 | 0.0808 | 13813 | 29004 | 3704 | 7957 |
| 2.5005 | 34.98 | 787 | 2.3661 | 0.5715 | 0.8503 | 0.7324 | 0.9105 | 0.0895 | 14454 | 28259 | 3808 | 7488 |
| 2.4067 | 36.0 | 810 | 2.3241 | 0.5436 | 0.8208 | 0.7145 | 0.8991 | 0.1009 | 15259 | 27414 | 3848 | 6923 |
| 2.3969 | 36.98 | 832 | 2.2875 | 0.5252 | 0.8022 | 0.7004 | 0.8890 | 0.1110 | 15966 | 26612 | 3943 | 6765 |
| 2.3816 | 38.0 | 855 | 2.2529 | 0.5033 | 0.7763 | 0.6837 | 0.8772 | 0.1228 | 16710 | 25846 | 3965 | 6302 |
| 2.3544 | 38.98 | 877 | 2.2230 | 0.4892 | 0.7597 | 0.6709 | 0.8673 | 0.1327 | 17339 | 25191 | 3991 | 6159 |
| 2.2744 | 40.0 | 900 | 2.1951 | 0.4719 | 0.7402 | 0.6570 | 0.8565 | 0.1435 | 17977 | 24541 | 4003 | 5893 |
| 2.2653 | 40.98 | 922 | 2.1715 | 0.4591 | 0.7252 | 0.6454 | 0.8470 | 0.1530 | 18532 | 23960 | 4029 | 5747 |
| 2.2736 | 42.0 | 945 | 2.1500 | 0.4461 | 0.7092 | 0.6345 | 0.8382 | 0.1618 | 19009 | 23522 | 3990 | 5482 |
| 2.256 | 42.98 | 967 | 2.1324 | 0.4347 | 0.6953 | 0.6240 | 0.8294 | 0.1706 | 19489 | 23062 | 3970 | 5313 |
| 2.2187 | 44.0 | 990 | 2.1170 | 0.4265 | 0.6852 | 0.6164 | 0.8227 | 0.1773 | 19834 | 22682 | 4005 | 5187 |
| 2.2122 | 44.98 | 1012 | 2.1050 | 0.4187 | 0.6755 | 0.6094 | 0.8166 | 0.1834 | 20141 | 22360 | 4020 | 5044 |
| 2.2259 | 46.0 | 1035 | 2.0954 | 0.4126 | 0.6690 | 0.6045 | 0.8123 | 0.1877 | 20360 | 22162 | 3999 | 4963 |
| 2.2367 | 46.98 | 1057 | 2.0889 | 0.4087 | 0.6642 | 0.6007 | 0.8088 | 0.1912 | 20538 | 21978 | 4005 | 4916 |
| 2.1789 | 48.0 | 1080 | 2.0849 | 0.4062 | 0.6617 | 0.5988 | 0.8071 | 0.1929 | 20623 | 21886 | 4012 | 4887 |
| 2.0912 | 48.89 | 1100 | 2.0838 | 0.4056 | 0.6611 | 0.5983 | 0.8066 | 0.1934 | 20649 | 21863 | 4009 | 4882 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.0+rocm5.6
- Datasets 2.18.0
- Tokenizers 0.15.2
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