---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-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-300m-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: 0.5156
- Wer: 0.3337

## 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 8.3411        | 0.66  | 500   | 3.5728          | 1.0    |
| 3.418         | 1.31  | 1000  | 3.1432          | 1.0    |
| 1.6726        | 1.97  | 1500  | 0.7995          | 0.7146 |
| 0.8244        | 2.62  | 2000  | 0.5569          | 0.5370 |
| 0.6392        | 3.28  | 2500  | 0.4773          | 0.4744 |
| 0.5537        | 3.94  | 3000  | 0.4592          | 0.4631 |
| 0.4956        | 4.59  | 3500  | 0.4649          | 0.4536 |
| 0.4539        | 5.25  | 4000  | 0.4345          | 0.4175 |
| 0.4144        | 5.91  | 4500  | 0.4291          | 0.4204 |
| 0.3899        | 6.56  | 5000  | 0.4325          | 0.4105 |
| 0.3748        | 7.22  | 5500  | 0.4151          | 0.3954 |
| 0.3543        | 7.87  | 6000  | 0.4320          | 0.4070 |
| 0.3335        | 8.53  | 6500  | 0.4061          | 0.3776 |
| 0.3266        | 9.19  | 7000  | 0.4307          | 0.3899 |
| 0.3107        | 9.84  | 7500  | 0.4404          | 0.3866 |
| 0.2886        | 10.5  | 8000  | 0.4528          | 0.3825 |
| 0.2897        | 11.15 | 8500  | 0.4027          | 0.3731 |
| 0.2757        | 11.81 | 9000  | 0.4423          | 0.3837 |
| 0.2582        | 12.47 | 9500  | 0.4412          | 0.3717 |
| 0.2598        | 13.12 | 10000 | 0.4410          | 0.3609 |
| 0.2421        | 13.78 | 10500 | 0.4398          | 0.3651 |
| 0.2414        | 14.44 | 11000 | 0.4488          | 0.3585 |
| 0.2259        | 15.09 | 11500 | 0.4528          | 0.3572 |
| 0.2269        | 15.75 | 12000 | 0.4613          | 0.3590 |
| 0.2109        | 16.4  | 12500 | 0.4492          | 0.3610 |
| 0.2097        | 17.06 | 13000 | 0.4468          | 0.3522 |
| 0.1992        | 17.72 | 13500 | 0.4520          | 0.3531 |
| 0.1949        | 18.37 | 14000 | 0.4782          | 0.3525 |
| 0.1924        | 19.03 | 14500 | 0.4643          | 0.3459 |
| 0.1906        | 19.69 | 15000 | 0.4839          | 0.3519 |
| 0.1837        | 20.34 | 15500 | 0.4891          | 0.3427 |
| 0.1744        | 21.0  | 16000 | 0.4905          | 0.3481 |
| 0.1705        | 21.65 | 16500 | 0.4758          | 0.3445 |
| 0.1697        | 22.31 | 17000 | 0.4765          | 0.3441 |
| 0.1657        | 22.97 | 17500 | 0.5059          | 0.3447 |
| 0.1582        | 23.62 | 18000 | 0.4941          | 0.3446 |
| 0.159         | 24.28 | 18500 | 0.4977          | 0.3469 |
| 0.1562        | 24.93 | 19000 | 0.4966          | 0.3415 |
| 0.1516        | 25.59 | 19500 | 0.5130          | 0.3403 |
| 0.144         | 26.25 | 20000 | 0.5049          | 0.3390 |
| 0.1429        | 26.9  | 20500 | 0.5130          | 0.3355 |
| 0.1378        | 27.56 | 21000 | 0.5140          | 0.3371 |
| 0.1436        | 28.22 | 21500 | 0.5172          | 0.3351 |
| 0.1363        | 28.87 | 22000 | 0.5215          | 0.3361 |
| 0.1332        | 29.53 | 22500 | 0.5156          | 0.3337 |


### Framework versions

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