---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300-vivos
  results: []
---

<!-- 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-300-vivos

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5745
- Wer: 0.3214

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 8.1425        | 0.66  | 500   | 3.5478          | 1.0    |
| 3.4041        | 1.31  | 1000  | 2.9316          | 1.0001 |
| 1.6144        | 1.97  | 1500  | 0.7917          | 0.6804 |
| 0.8284        | 2.62  | 2000  | 0.5468          | 0.5401 |
| 0.6356        | 3.28  | 2500  | 0.4703          | 0.4812 |
| 0.553         | 3.94  | 3000  | 0.4371          | 0.4597 |
| 0.4903        | 4.59  | 3500  | 0.4748          | 0.4622 |
| 0.4524        | 5.25  | 4000  | 0.4442          | 0.4235 |
| 0.4107        | 5.91  | 4500  | 0.4354          | 0.4219 |
| 0.3869        | 6.56  | 5000  | 0.4204          | 0.4084 |
| 0.3711        | 7.22  | 5500  | 0.4053          | 0.3917 |
| 0.3507        | 7.87  | 6000  | 0.4134          | 0.3930 |
| 0.3396        | 8.53  | 6500  | 0.4040          | 0.3834 |
| 0.3284        | 9.19  | 7000  | 0.4278          | 0.3961 |
| 0.3096        | 9.84  | 7500  | 0.4590          | 0.3877 |
| 0.2878        | 10.5  | 8000  | 0.4369          | 0.3761 |
| 0.2872        | 11.15 | 8500  | 0.4224          | 0.3759 |
| 0.2756        | 11.81 | 9000  | 0.4442          | 0.3778 |
| 0.2618        | 12.47 | 9500  | 0.4504          | 0.3832 |
| 0.2658        | 13.12 | 10000 | 0.4431          | 0.3677 |
| 0.245         | 13.78 | 10500 | 0.4491          | 0.3684 |
| 0.2467        | 14.44 | 11000 | 0.4436          | 0.3553 |
| 0.2289        | 15.09 | 11500 | 0.4655          | 0.3649 |
| 0.2332        | 15.75 | 12000 | 0.4396          | 0.3530 |
| 0.2205        | 16.4  | 12500 | 0.4577          | 0.3605 |
| 0.2181        | 17.06 | 13000 | 0.4662          | 0.3544 |
| 0.2081        | 17.72 | 13500 | 0.4979          | 0.3617 |
| 0.2009        | 18.37 | 14000 | 0.4564          | 0.3598 |
| 0.1997        | 19.03 | 14500 | 0.4696          | 0.3526 |
| 0.1946        | 19.69 | 15000 | 0.5036          | 0.3590 |
| 0.1937        | 20.34 | 15500 | 0.4763          | 0.3565 |
| 0.1848        | 21.0  | 16000 | 0.5059          | 0.3564 |
| 0.1821        | 21.65 | 16500 | 0.5048          | 0.3622 |
| 0.1784        | 22.31 | 17000 | 0.5252          | 0.3588 |
| 0.1758        | 22.97 | 17500 | 0.4968          | 0.3482 |
| 0.1665        | 23.62 | 18000 | 0.5142          | 0.3511 |
| 0.1661        | 24.28 | 18500 | 0.5230          | 0.3507 |
| 0.1625        | 24.93 | 19000 | 0.5133          | 0.3476 |
| 0.1601        | 25.59 | 19500 | 0.5045          | 0.3406 |
| 0.1521        | 26.25 | 20000 | 0.5205          | 0.3472 |
| 0.1474        | 26.9  | 20500 | 0.5262          | 0.3481 |
| 0.1442        | 27.56 | 21000 | 0.5167          | 0.3393 |
| 0.1487        | 28.22 | 21500 | 0.5420          | 0.3467 |
| 0.1403        | 28.87 | 22000 | 0.5737          | 0.3548 |
| 0.1365        | 29.53 | 22500 | 0.5168          | 0.3359 |
| 0.133         | 30.18 | 23000 | 0.5551          | 0.3394 |
| 0.1372        | 30.84 | 23500 | 0.5464          | 0.3471 |
| 0.1313        | 31.5  | 24000 | 0.5537          | 0.3425 |
| 0.1275        | 32.15 | 24500 | 0.5673          | 0.3366 |
| 0.1177        | 32.81 | 25000 | 0.5440          | 0.3375 |
| 0.1231        | 33.46 | 25500 | 0.5436          | 0.3353 |
| 0.121         | 34.12 | 26000 | 0.5624          | 0.3333 |
| 0.1152        | 34.78 | 26500 | 0.5686          | 0.3415 |
| 0.117         | 35.43 | 27000 | 0.5517          | 0.3390 |
| 0.1139        | 36.09 | 27500 | 0.5543          | 0.3304 |
| 0.1089        | 36.75 | 28000 | 0.5630          | 0.3348 |
| 0.1159        | 37.4  | 28500 | 0.5635          | 0.3366 |
| 0.1115        | 38.06 | 29000 | 0.5657          | 0.3350 |
| 0.1068        | 38.71 | 29500 | 0.5782          | 0.3348 |
| 0.1026        | 39.37 | 30000 | 0.5721          | 0.3282 |
| 0.1058        | 40.03 | 30500 | 0.5746          | 0.3339 |
| 0.1017        | 40.68 | 31000 | 0.5727          | 0.3265 |
| 0.099         | 41.34 | 31500 | 0.5721          | 0.3309 |
| 0.1008        | 41.99 | 32000 | 0.5543          | 0.3274 |
| 0.0957        | 42.65 | 32500 | 0.5642          | 0.3245 |
| 0.0921        | 43.31 | 33000 | 0.5768          | 0.3239 |
| 0.0941        | 43.96 | 33500 | 0.5649          | 0.3235 |
| 0.0927        | 44.62 | 34000 | 0.5659          | 0.3250 |
| 0.0899        | 45.28 | 34500 | 0.5680          | 0.3193 |
| 0.0898        | 45.93 | 35000 | 0.5643          | 0.3212 |
| 0.0864        | 46.59 | 35500 | 0.5769          | 0.3250 |
| 0.0941        | 47.24 | 36000 | 0.5726          | 0.3247 |
| 0.0882        | 47.9  | 36500 | 0.5804          | 0.3250 |
| 0.086         | 48.56 | 37000 | 0.5762          | 0.3225 |
| 0.0861        | 49.21 | 37500 | 0.5748          | 0.3234 |
| 0.0842        | 49.87 | 38000 | 0.5745          | 0.3214 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2