---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: es_ar
  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. -->

# es_ar

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4940
- Wer: 0.3263
- Cer: 0.2414

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.6158        | 0.4   | 500   | 3.7004          | 0.9886 | 0.9625 |
| 1.0731        | 0.8   | 1000  | 1.0090          | 0.7501 | 0.4066 |
| 0.7529        | 1.2   | 1500  | 0.7609          | 0.6195 | 0.3460 |
| 0.6441        | 1.6   | 2000  | 0.6322          | 0.5524 | 0.3171 |
| 0.607         | 2.0   | 2500  | 0.5795          | 0.5202 | 0.3065 |
| 0.4744        | 2.4   | 3000  | 0.5848          | 0.5096 | 0.3056 |
| 0.4604        | 2.8   | 3500  | 0.5341          | 0.4666 | 0.2907 |
| 0.3763        | 3.2   | 4000  | 0.5060          | 0.4416 | 0.2812 |
| 0.3952        | 3.6   | 4500  | 0.5214          | 0.4566 | 0.2850 |
| 0.3962        | 4.0   | 5000  | 0.4890          | 0.4324 | 0.2784 |
| 0.3137        | 4.4   | 5500  | 0.4833          | 0.4165 | 0.2713 |
| 0.316         | 4.8   | 6000  | 0.5005          | 0.4182 | 0.2738 |
| 0.2721        | 5.2   | 6500  | 0.4961          | 0.4171 | 0.2732 |
| 0.2561        | 5.6   | 7000  | 0.4742          | 0.3997 | 0.2645 |
| 0.2854        | 6.0   | 7500  | 0.4600          | 0.3991 | 0.2662 |
| 0.2599        | 6.4   | 8000  | 0.4541          | 0.4022 | 0.2659 |
| 0.2249        | 6.8   | 8500  | 0.4586          | 0.3911 | 0.2615 |
| 0.1931        | 7.2   | 9000  | 0.4721          | 0.3871 | 0.2614 |
| 0.195         | 7.6   | 9500  | 0.4636          | 0.3898 | 0.2608 |
| 0.1991        | 8.0   | 10000 | 0.4259          | 0.3716 | 0.2555 |
| 0.1657        | 8.4   | 10500 | 0.4548          | 0.3714 | 0.2573 |
| 0.1802        | 8.8   | 11000 | 0.4540          | 0.3582 | 0.2526 |
| 0.1359        | 9.2   | 11500 | 0.4685          | 0.3652 | 0.2552 |
| 0.1419        | 9.6   | 12000 | 0.4524          | 0.3561 | 0.2512 |
| 0.1531        | 10.0  | 12500 | 0.4443          | 0.3578 | 0.2514 |
| 0.1313        | 10.4  | 13000 | 0.4536          | 0.3536 | 0.2495 |
| 0.1269        | 10.8  | 13500 | 0.4563          | 0.3517 | 0.2480 |
| 0.102         | 11.2  | 14000 | 0.4606          | 0.3424 | 0.2476 |
| 0.103         | 11.6  | 14500 | 0.4611          | 0.3489 | 0.2477 |
| 0.1088        | 12.0  | 15000 | 0.4505          | 0.3362 | 0.2447 |
| 0.0917        | 12.4  | 15500 | 0.4741          | 0.3404 | 0.2458 |
| 0.0847        | 12.8  | 16000 | 0.4714          | 0.3340 | 0.2440 |
| 0.0768        | 13.2  | 16500 | 0.4943          | 0.3286 | 0.2427 |
| 0.0789        | 13.6  | 17000 | 0.4813          | 0.3308 | 0.2429 |
| 0.0797        | 14.0  | 17500 | 0.4861          | 0.3288 | 0.2423 |
| 0.0698        | 14.4  | 18000 | 0.5003          | 0.3271 | 0.2416 |
| 0.0686        | 14.8  | 18500 | 0.4940          | 0.3263 | 0.2414 |


### Framework versions

- Transformers 4.36.2
- Pytorch 1.13.0+cu116
- Datasets 2.15.0
- Tokenizers 0.15.2