---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-1b-mecita-portuguese-all-text-a_coisa-os_morcegos
  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-xlsr-1b-mecita-portuguese-all-text-a_coisa-os_morcegos

This model is a fine-tuned version of [jonatasgrosman/wav2vec2-xls-r-1b-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1774
- Wer: 0.0844
- Cer: 0.0266

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 25.5905       | 1.0   | 79   | 0.4495          | 0.2580 | 0.0734 |
| 3.1482        | 2.0   | 158  | 0.2479          | 0.1204 | 0.0380 |
| 0.4247        | 3.0   | 237  | 0.2347          | 0.1025 | 0.0345 |
| 0.3136        | 4.0   | 316  | 0.2044          | 0.1017 | 0.0322 |
| 0.3136        | 5.0   | 395  | 0.1906          | 0.0930 | 0.0296 |
| 0.2985        | 6.0   | 474  | 0.2050          | 0.0963 | 0.0311 |
| 0.2413        | 7.0   | 553  | 0.2025          | 0.0971 | 0.0309 |
| 0.2267        | 8.0   | 632  | 0.2006          | 0.0885 | 0.0291 |
| 0.224         | 9.0   | 711  | 0.1991          | 0.0917 | 0.0291 |
| 0.224         | 10.0  | 790  | 0.1881          | 0.0885 | 0.0281 |
| 0.1864        | 11.0  | 869  | 0.1841          | 0.0893 | 0.0278 |
| 0.1951        | 12.0  | 948  | 0.1809          | 0.0895 | 0.0282 |
| 0.1794        | 13.0  | 1027 | 0.1923          | 0.0833 | 0.0280 |
| 0.1621        | 14.0  | 1106 | 0.1949          | 0.0857 | 0.0277 |
| 0.1621        | 15.0  | 1185 | 0.1929          | 0.0817 | 0.0266 |
| 0.1695        | 16.0  | 1264 | 0.1907          | 0.0839 | 0.0270 |
| 0.1528        | 17.0  | 1343 | 0.1839          | 0.0906 | 0.0286 |
| 0.1592        | 18.0  | 1422 | 0.1866          | 0.0903 | 0.0281 |
| 0.1519        | 19.0  | 1501 | 0.2031          | 0.0857 | 0.0275 |
| 0.1519        | 20.0  | 1580 | 0.1948          | 0.0860 | 0.0278 |
| 0.1257        | 21.0  | 1659 | 0.1850          | 0.0860 | 0.0262 |
| 0.1288        | 22.0  | 1738 | 0.1774          | 0.0844 | 0.0266 |
| 0.115         | 23.0  | 1817 | 0.1960          | 0.0844 | 0.0265 |
| 0.115         | 24.0  | 1896 | 0.1832          | 0.0825 | 0.0258 |
| 0.1223        | 25.0  | 1975 | 0.1920          | 0.0828 | 0.0261 |
| 0.1175        | 26.0  | 2054 | 0.1951          | 0.0803 | 0.0260 |
| 0.1051        | 27.0  | 2133 | 0.1996          | 0.0825 | 0.0266 |
| 0.1033        | 28.0  | 2212 | 0.2152          | 0.0847 | 0.0274 |
| 0.1033        | 29.0  | 2291 | 0.2082          | 0.0879 | 0.0277 |
| 0.0961        | 30.0  | 2370 | 0.2153          | 0.0855 | 0.0274 |
| 0.1003        | 31.0  | 2449 | 0.2044          | 0.0903 | 0.0288 |
| 0.1129        | 32.0  | 2528 | 0.2050          | 0.0855 | 0.0268 |
| 0.0939        | 33.0  | 2607 | 0.2028          | 0.0860 | 0.0271 |
| 0.0939        | 34.0  | 2686 | 0.2031          | 0.0847 | 0.0274 |
| 0.0846        | 35.0  | 2765 | 0.2046          | 0.0822 | 0.0269 |
| 0.083         | 36.0  | 2844 | 0.2094          | 0.0825 | 0.0265 |
| 0.0844        | 37.0  | 2923 | 0.2176          | 0.0820 | 0.0268 |
| 0.0829        | 38.0  | 3002 | 0.2082          | 0.0817 | 0.0267 |
| 0.0829        | 39.0  | 3081 | 0.2200          | 0.0893 | 0.0286 |
| 0.103         | 40.0  | 3160 | 0.2102          | 0.0841 | 0.0276 |
| 0.0728        | 41.0  | 3239 | 0.2143          | 0.0817 | 0.0271 |
| 0.079         | 42.0  | 3318 | 0.2131          | 0.0825 | 0.0265 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.0
- Tokenizers 0.13.3