---
library_name: transformers
language:
- lg
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- yogera
metrics:
- wer
model-index:
- name: wav2vec2-bert
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Yogera
      type: yogera
    metrics:
    - name: Wer
      type: wer
      value: 0.14867316851893853
---

<!-- 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-bert

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the Yogera dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2216
- Wer: 0.1487
- Cer: 0.0334

## 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: 5e-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
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.6681        | 1.0   | 235  | 0.2226          | 0.2616 | 0.0533 |
| 0.1666        | 2.0   | 470  | 0.1639          | 0.2013 | 0.0410 |
| 0.1249        | 3.0   | 705  | 0.1608          | 0.1912 | 0.0416 |
| 0.101         | 4.0   | 940  | 0.1573          | 0.1835 | 0.0416 |
| 0.0833        | 5.0   | 1175 | 0.1567          | 0.1697 | 0.0378 |
| 0.0715        | 6.0   | 1410 | 0.1589          | 0.1564 | 0.0346 |
| 0.0624        | 7.0   | 1645 | 0.1634          | 0.1728 | 0.0408 |
| 0.0541        | 8.0   | 1880 | 0.1592          | 0.1559 | 0.0341 |
| 0.0464        | 9.0   | 2115 | 0.1788          | 0.1546 | 0.0336 |
| 0.0434        | 10.0  | 2350 | 0.1641          | 0.1575 | 0.0353 |
| 0.0385        | 11.0  | 2585 | 0.1854          | 0.1498 | 0.0333 |
| 0.0358        | 12.0  | 2820 | 0.1915          | 0.1504 | 0.0345 |
| 0.0308        | 13.0  | 3055 | 0.1747          | 0.1514 | 0.0328 |
| 0.0283        | 14.0  | 3290 | 0.1966          | 0.1449 | 0.0329 |
| 0.0274        | 15.0  | 3525 | 0.1882          | 0.1535 | 0.0342 |
| 0.0246        | 16.0  | 3760 | 0.2199          | 0.1588 | 0.0362 |
| 0.0212        | 17.0  | 3995 | 0.2108          | 0.1572 | 0.0355 |
| 0.0188        | 18.0  | 4230 | 0.2173          | 0.1453 | 0.0320 |
| 0.017         | 19.0  | 4465 | 0.2358          | 0.1444 | 0.0324 |
| 0.0177        | 20.0  | 4700 | 0.2280          | 0.1548 | 0.0339 |
| 0.0174        | 21.0  | 4935 | 0.2142          | 0.1484 | 0.0322 |
| 0.0138        | 22.0  | 5170 | 0.2315          | 0.1489 | 0.0338 |
| 0.0122        | 23.0  | 5405 | 0.2116          | 0.1483 | 0.0341 |
| 0.0125        | 24.0  | 5640 | 0.2216          | 0.1487 | 0.0334 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.1.0+cu118
- Datasets 3.0.1
- Tokenizers 0.20.1