transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-1b-mecita-portuguese-all-05
  results: []
---

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# wav2vec2-xlsr-1b-mecita-portuguese-all-05

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: 0.1759
- Wer: 0.0948
- Cer: 0.0332

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 27.2023       | 1.0   | 86   | 0.3596          | 0.2338 | 0.0682 |
| 3.376         | 2.0   | 172  | 0.2263          | 0.1195 | 0.0414 |
| 0.4098        | 3.0   | 258  | 0.2067          | 0.1091 | 0.0387 |
| 0.3262        | 4.0   | 344  | 0.2059          | 0.1081 | 0.0380 |
| 0.3068        | 5.0   | 430  | 0.1949          | 0.1016 | 0.0360 |
| 0.239         | 6.0   | 516  | 0.1934          | 0.0948 | 0.0346 |
| 0.2351        | 7.0   | 602  | 0.1824          | 0.0946 | 0.0339 |
| 0.2351        | 8.0   | 688  | 0.1759          | 0.0948 | 0.0332 |
| 0.2205        | 9.0   | 774  | 0.1813          | 0.0902 | 0.0330 |
| 0.2055        | 10.0  | 860  | 0.1898          | 0.0892 | 0.0321 |
| 0.1882        | 11.0  | 946  | 0.1877          | 0.0912 | 0.0330 |
| 0.1787        | 12.0  | 1032 | 0.1844          | 0.0921 | 0.0321 |
| 0.1622        | 13.0  | 1118 | 0.1839          | 0.0861 | 0.0318 |
| 0.1676        | 14.0  | 1204 | 0.1852          | 0.0846 | 0.0316 |
| 0.1676        | 15.0  | 1290 | 0.1801          | 0.0902 | 0.0324 |
| 0.165         | 16.0  | 1376 | 0.1870          | 0.0810 | 0.0309 |
| 0.143         | 17.0  | 1462 | 0.1834          | 0.0827 | 0.0310 |
| 0.1329        | 18.0  | 1548 | 0.1815          | 0.0815 | 0.0309 |
| 0.149         | 19.0  | 1634 | 0.1822          | 0.0817 | 0.0310 |
| 0.1251        | 20.0  | 1720 | 0.1831          | 0.0824 | 0.0306 |
| 0.1243        | 21.0  | 1806 | 0.1819          | 0.0817 | 0.0304 |
| 0.1243        | 22.0  | 1892 | 0.1859          | 0.0854 | 0.0309 |
| 0.1195        | 23.0  | 1978 | 0.1841          | 0.0803 | 0.0300 |
| 0.1214        | 24.0  | 2064 | 0.1881          | 0.0824 | 0.0317 |
| 0.1179        | 25.0  | 2150 | 0.1898          | 0.0824 | 0.0309 |
| 0.1004        | 26.0  | 2236 | 0.2005          | 0.0863 | 0.0322 |
| 0.1255        | 27.0  | 2322 | 0.1982          | 0.0834 | 0.0318 |
| 0.1169        | 28.0  | 2408 | 0.1947          | 0.0873 | 0.0324 |


### Framework versions

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