---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
- asr
- w2v-bert-2.0
datasets:
- common_voice_16_1
metrics:
- wer
- cer
- bertscore
model-index:
- name: w2v-bert-2.0-pt_pt_v2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: pt
      split: validation
      args: pt
    metrics:
    - name: Wer
      type: wer
      value: 0.08315087821729188
language:
- pt
---

<!-- 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. -->

# w2v-bert-2.0-pt_pt_v2

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_16_1 Portuguese subset using 1XRTX 3090.
It achieves the following results on the test set:
- Wer: 0.10491320595991134
- Cer: 0.032070871626631914
- Bert Score: 0.9619712047981167
- Sentence Similarity: 0.93867844

## 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
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    | Bert Score |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:----------:|
| 1.2735        | 1.0   | 678  | 0.2292          | 0.1589 | 0.0415 | 0.9498     |
| 0.1715        | 2.0   | 1356 | 0.1762          | 0.1283 | 0.0344 | 0.9599     |
| 0.1158        | 3.0   | 2034 | 0.1539          | 0.1100 | 0.0298 | 0.9646     |
| 0.0821        | 4.0   | 2712 | 0.1362          | 0.0949 | 0.0258 | 0.9703     |
| 0.0605        | 5.0   | 3390 | 0.1349          | 0.0860 | 0.0236 | 0.9728     |
| 0.0475        | 6.0   | 4068 | 0.1395          | 0.0871 | 0.0239 | 0.9728     |
| 0.0355        | 7.0   | 4746 | 0.1487          | 0.0837 | 0.0230 | 0.9739     |
| 0.0309        | 8.0   | 5424 | 0.1452          | 0.0873 | 0.0240 | 0.9728     |
| 0.0308        | 9.0   | 6102 | 0.1390          | 0.0843 | 0.0228 | 0.9735     |
| 0.0239        | 10.0  | 6780 | 0.1282          | 0.0832 | 0.0224 | 0.9739     |

### Evaluation results

| Test Wer           | Test Cer            | Test Bert Score   | Runtime | Samples per second    | 
|:------------------:|:-------------------:|:-----------------:|:-------:|:---------------------:|
| 0.09146400542583083| 0.02643665913309742 | 0.9702128323433327| 266.8185| 35.282                |

### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.0
- Datasets 2.18.0
- Tokenizers 0.15.2