---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_15_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-br
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_15_0
      type: common_voice_15_0
      config: br
      split: None
      args: br
    metrics:
    - name: Wer
      type: wer
      value: 50.08524001794527
---

<!-- 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: 0.8404
- Wer: 50.0852
- Cer: 17.4519

## 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: 4e-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: 300
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Cer     | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|
| 6.6871        | 1.09  | 500   | 100.0   | 3.2774          | 100.0   |
| 3.0612        | 2.18  | 1000  | 99.9339 | 2.7879          | 99.9910 |
| 1.7934        | 3.27  | 1500  | 29.4362 | 1.1762          | 80.5922 |
| 1.0914        | 4.36  | 2000  | 25.0591 | 0.9210          | 70.7941 |
| 0.8895        | 5.45  | 2500  | 23.6321 | 0.8364          | 67.1243 |
| 0.7831        | 6.54  | 3000  | 22.4169 | 0.7813          | 63.9480 |
| 0.697         | 7.63  | 3500  | 21.4625 | 0.7820          | 61.8214 |
| 0.6474        | 8.71  | 4000  | 20.7367 | 0.7471          | 59.4437 |
| 0.5969        | 9.8   | 4500  | 20.0072 | 0.7255          | 57.8914 |
| 0.5677        | 10.89 | 5000  | 20.0563 | 0.7440          | 57.5774 |
| 0.5286        | 11.98 | 5500  | 19.7483 | 0.7622          | 56.2494 |
| 0.5054        | 13.07 | 6000  | 19.1510 | 0.7318          | 55.1548 |
| 0.4831        | 14.16 | 6500  | 19.2096 | 0.7731          | 54.6882 |
| 0.4606        | 15.25 | 7000  | 19.0282 | 0.7457          | 54.4459 |
| 0.4432        | 16.34 | 7500  | 18.9923 | 0.7638          | 54.1319 |
| 0.4116        | 17.43 | 8000  | 18.6880 | 0.7576          | 53.3692 |
| 0.4099        | 18.52 | 8500  | 18.6653 | 0.7944          | 53.1359 |
| 0.3991        | 19.61 | 9000  | 18.7258 | 0.8229          | 52.9296 |
| 0.3796        | 20.7  | 9500  | 18.4555 | 0.8106          | 52.3194 |
| 0.3715        | 21.79 | 10000 | 18.1078 | 0.7611          | 51.8798 |
| 0.359         | 22.88 | 10500 | 18.4139 | 0.7921          | 52.2207 |
| 0.3384        | 23.97 | 11000 | 18.0624 | 0.8022          | 51.4850 |
| 0.3367        | 25.05 | 11500 | 0.7921  | 51.5209         | 18.0322 |
| 0.3295        | 26.14 | 12000 | 0.8354  | 51.4491         | 17.9811 |
| 0.3183        | 27.23 | 12500 | 0.8171  | 51.0991         | 17.8488 |
| 0.3135        | 28.32 | 13000 | 0.8094  | 50.9915         | 17.7354 |
| 0.309         | 29.41 | 13500 | 0.8632  | 50.8659         | 17.7978 |
| 0.2922        | 30.5  | 14000 | 0.8268  | 50.7672         | 17.6636 |
| 0.2987        | 31.59 | 14500 | 0.8108  | 50.2557         | 17.5918 |
| 0.2914        | 32.68 | 15000 | 0.8237  | 50.0224         | 17.4708 |
| 0.2893        | 33.77 | 15500 | 0.8450  | 50.1211         | 17.3877 |
| 0.2853        | 34.86 | 16000 | 0.8354  | 50.4800         | 17.5464 |
| 0.2791        | 35.95 | 16500 | 0.8424  | 50.1929         | 17.5257 |
| 0.2732        | 37.04 | 17000 | 0.8390  | 50.2826         | 17.5653 |
| 0.2691        | 38.13 | 17500 | 0.8420  | 50.1122         | 17.4671 |
| 0.2702        | 39.22 | 18000 | 0.8404  | 50.0852         | 17.4519 |


### Framework versions

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