---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-mecita-coraa-portuguese-random-all-03
  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. -->

# wav2vec2-large-xlsr-mecita-coraa-portuguese-random-all-03

This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1364
- Wer: 0.0844
- Cer: 0.0267

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 29.1535       | 1.0   | 86   | 3.1676          | 1.0    | 1.0    |
| 7.924         | 2.0   | 172  | 2.9289          | 1.0    | 1.0    |
| 3.0115        | 3.0   | 258  | 2.8972          | 1.0    | 1.0    |
| 2.9304        | 4.0   | 344  | 2.8877          | 1.0    | 1.0    |
| 2.9073        | 5.0   | 430  | 2.8574          | 1.0    | 1.0    |
| 2.7919        | 6.0   | 516  | 1.9144          | 1.0    | 0.6742 |
| 1.6061        | 7.0   | 602  | 0.5439          | 0.2886 | 0.0766 |
| 1.6061        | 8.0   | 688  | 0.3726          | 0.1949 | 0.0558 |
| 0.7808        | 9.0   | 774  | 0.2960          | 0.1818 | 0.0517 |
| 0.5543        | 10.0  | 860  | 0.2591          | 0.1688 | 0.0477 |
| 0.4721        | 11.0  | 946  | 0.2367          | 0.1445 | 0.0427 |
| 0.414         | 12.0  | 1032 | 0.2167          | 0.1260 | 0.0376 |
| 0.3819        | 13.0  | 1118 | 0.1979          | 0.1150 | 0.0350 |
| 0.3376        | 14.0  | 1204 | 0.1877          | 0.1169 | 0.0346 |
| 0.3376        | 15.0  | 1290 | 0.1766          | 0.1084 | 0.0335 |
| 0.3199        | 16.0  | 1376 | 0.1754          | 0.1032 | 0.0323 |
| 0.3174        | 17.0  | 1462 | 0.1697          | 0.1017 | 0.0315 |
| 0.2747        | 18.0  | 1548 | 0.1668          | 0.0963 | 0.0308 |
| 0.2618        | 19.0  | 1634 | 0.1626          | 0.0937 | 0.0301 |
| 0.2557        | 20.0  | 1720 | 0.1597          | 0.0946 | 0.0299 |
| 0.2578        | 21.0  | 1806 | 0.1585          | 0.0944 | 0.0296 |
| 0.2578        | 22.0  | 1892 | 0.1549          | 0.0965 | 0.0302 |
| 0.2288        | 23.0  | 1978 | 0.1501          | 0.0939 | 0.0284 |
| 0.2269        | 24.0  | 2064 | 0.1524          | 0.0944 | 0.0291 |
| 0.2327        | 25.0  | 2150 | 0.1476          | 0.0903 | 0.0281 |
| 0.2024        | 26.0  | 2236 | 0.1481          | 0.0903 | 0.0284 |
| 0.2056        | 27.0  | 2322 | 0.1434          | 0.0925 | 0.0284 |
| 0.2097        | 28.0  | 2408 | 0.1468          | 0.0894 | 0.0280 |
| 0.2097        | 29.0  | 2494 | 0.1435          | 0.0860 | 0.0273 |
| 0.2177        | 30.0  | 2580 | 0.1498          | 0.0877 | 0.0281 |
| 0.1935        | 31.0  | 2666 | 0.1452          | 0.0891 | 0.0278 |
| 0.1918        | 32.0  | 2752 | 0.1466          | 0.0849 | 0.0275 |
| 0.1805        | 33.0  | 2838 | 0.1437          | 0.0889 | 0.0282 |
| 0.1805        | 34.0  | 2924 | 0.1409          | 0.0870 | 0.0274 |
| 0.1835        | 35.0  | 3010 | 0.1422          | 0.0856 | 0.0271 |
| 0.1835        | 36.0  | 3096 | 0.1377          | 0.0851 | 0.0264 |
| 0.1787        | 37.0  | 3182 | 0.1364          | 0.0844 | 0.0267 |
| 0.1695        | 38.0  | 3268 | 0.1418          | 0.0849 | 0.0268 |
| 0.1775        | 39.0  | 3354 | 0.1401          | 0.0844 | 0.0270 |
| 0.1763        | 40.0  | 3440 | 0.1402          | 0.0815 | 0.0265 |
| 0.1702        | 41.0  | 3526 | 0.1418          | 0.0830 | 0.0264 |
| 0.1569        | 42.0  | 3612 | 0.1400          | 0.0825 | 0.0258 |
| 0.1569        | 43.0  | 3698 | 0.1401          | 0.0815 | 0.0262 |
| 0.1617        | 44.0  | 3784 | 0.1406          | 0.0792 | 0.0262 |
| 0.1596        | 45.0  | 3870 | 0.1395          | 0.0818 | 0.0264 |
| 0.1431        | 46.0  | 3956 | 0.1382          | 0.0815 | 0.0262 |
| 0.158         | 47.0  | 4042 | 0.1391          | 0.0813 | 0.0265 |
| 0.1552        | 48.0  | 4128 | 0.1393          | 0.0825 | 0.0266 |
| 0.1379        | 49.0  | 4214 | 0.1371          | 0.0811 | 0.0256 |
| 0.145         | 50.0  | 4300 | 0.1392          | 0.0801 | 0.0256 |
| 0.145         | 51.0  | 4386 | 0.1416          | 0.0820 | 0.0262 |
| 0.1647        | 52.0  | 4472 | 0.1392          | 0.0789 | 0.0256 |
| 0.1493        | 53.0  | 4558 | 0.1425          | 0.0794 | 0.0257 |
| 0.1492        | 54.0  | 4644 | 0.1419          | 0.0796 | 0.0257 |
| 0.139         | 55.0  | 4730 | 0.1400          | 0.0758 | 0.0250 |
| 0.1385        | 56.0  | 4816 | 0.1424          | 0.0792 | 0.0253 |
| 0.128         | 57.0  | 4902 | 0.1403          | 0.0806 | 0.0253 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.13.3