---
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/wav2vec2-xls-r-300m
datasets:
- common_voice_15_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-br
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_15_0
      type: common_voice_15_0
      config: br
      split: None
      args: br
    metrics:
    - type: wer
      value: 49.79811574697174
      name: Wer
---

<!-- 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-xls-r-300m-br

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on the common_voice_15_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8887
- Wer: 49.7981
- Cer: 17.3877

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     | Cer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 5.1153        | 2.18  | 1000  | 2.8854          | 100.0   | 100.0   |
| 1.4117        | 4.36  | 2000  | 0.9161          | 71.2786 | 25.3180 |
| 0.7888        | 6.54  | 3000  | 0.7753          | 62.7456 | 22.0767 |
| 0.6316        | 8.71  | 4000  | 0.7550          | 58.1786 | 20.5383 |
| 0.5434        | 10.89 | 5000  | 0.7508          | 56.5096 | 20.1168 |
| 0.4672        | 13.07 | 6000  | 0.7844          | 54.9125 | 19.3835 |
| 0.4237        | 15.25 | 7000  | 0.7786          | 53.2705 | 18.5765 |
| 0.3899        | 17.43 | 8000  | 0.8050          | 53.0552 | 18.6105 |
| 0.3607        | 19.61 | 9000  | 0.8280          | 51.9874 | 18.3024 |
| 0.3355        | 21.79 | 10000 | 0.7967          | 51.5388 | 17.9811 |
| 0.3098        | 23.97 | 11000 | 0.8296          | 51.2876 | 17.9547 |
| 0.2937        | 26.14 | 12000 | 0.8544          | 50.9915 | 17.7827 |
| 0.2793        | 28.32 | 13000 | 0.8909          | 51.5478 | 18.1286 |
| 0.2641        | 30.5  | 14000 | 0.8740          | 50.4800 | 17.6561 |
| 0.2552        | 32.68 | 15000 | 0.8832          | 49.9776 | 17.4463 |
| 0.2467        | 34.86 | 16000 | 0.8753          | 50.3096 | 17.4765 |
| 0.2378        | 37.04 | 17000 | 0.8895          | 49.8789 | 17.3952 |
| 0.2337        | 39.22 | 18000 | 0.8887          | 49.7981 | 17.3877 |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2