---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-spanish-custom
  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-large-xls-r-300m-spanish-custom

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 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4426
- Wer: 0.2117

## 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: 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.2307        | 0.4   | 400   | 1.4431          | 0.9299 |
| 0.7066        | 0.79  | 800   | 0.5928          | 0.4836 |
| 0.4397        | 1.19  | 1200  | 0.4341          | 0.3730 |
| 0.3889        | 1.58  | 1600  | 0.4063          | 0.3499 |
| 0.3607        | 1.98  | 2000  | 0.3834          | 0.3235 |
| 0.2866        | 2.37  | 2400  | 0.3885          | 0.3163 |
| 0.2833        | 2.77  | 2800  | 0.3765          | 0.3140 |
| 0.2692        | 3.17  | 3200  | 0.3849          | 0.3132 |
| 0.2435        | 3.56  | 3600  | 0.3779          | 0.2984 |
| 0.2404        | 3.96  | 4000  | 0.3756          | 0.2934 |
| 0.2153        | 4.35  | 4400  | 0.3770          | 0.3075 |
| 0.2087        | 4.75  | 4800  | 0.3819          | 0.3022 |
| 0.1999        | 5.14  | 5200  | 0.3756          | 0.2959 |
| 0.1838        | 5.54  | 5600  | 0.3827          | 0.2858 |
| 0.1892        | 5.93  | 6000  | 0.3714          | 0.2999 |
| 0.1655        | 6.33  | 6400  | 0.3814          | 0.2812 |
| 0.1649        | 6.73  | 6800  | 0.3685          | 0.2727 |
| 0.1668        | 7.12  | 7200  | 0.3832          | 0.2825 |
| 0.1487        | 7.52  | 7600  | 0.3848          | 0.2788 |
| 0.152         | 7.91  | 8000  | 0.3810          | 0.2787 |
| 0.143         | 8.31  | 8400  | 0.3885          | 0.2856 |
| 0.1353        | 8.7   | 8800  | 0.4103          | 0.2827 |
| 0.1386        | 9.1   | 9200  | 0.4142          | 0.2874 |
| 0.1222        | 9.5   | 9600  | 0.3983          | 0.2830 |
| 0.1288        | 9.89  | 10000 | 0.4179          | 0.2781 |
| 0.1199        | 10.29 | 10400 | 0.4035          | 0.2789 |
| 0.1196        | 10.68 | 10800 | 0.4043          | 0.2746 |
| 0.1169        | 11.08 | 11200 | 0.4105          | 0.2753 |
| 0.1076        | 11.47 | 11600 | 0.4298          | 0.2686 |
| 0.1124        | 11.87 | 12000 | 0.4025          | 0.2704 |
| 0.1043        | 12.26 | 12400 | 0.4209          | 0.2659 |
| 0.0976        | 12.66 | 12800 | 0.4070          | 0.2672 |
| 0.1012        | 13.06 | 13200 | 0.4161          | 0.2720 |
| 0.0872        | 13.45 | 13600 | 0.4245          | 0.2697 |
| 0.0933        | 13.85 | 14000 | 0.4295          | 0.2684 |
| 0.0881        | 14.24 | 14400 | 0.4011          | 0.2650 |
| 0.0848        | 14.64 | 14800 | 0.3991          | 0.2675 |
| 0.0852        | 15.03 | 15200 | 0.4166          | 0.2617 |
| 0.0825        | 15.43 | 15600 | 0.4188          | 0.2639 |
| 0.081         | 15.83 | 16000 | 0.4181          | 0.2547 |
| 0.0753        | 16.22 | 16400 | 0.4103          | 0.2560 |
| 0.0747        | 16.62 | 16800 | 0.4017          | 0.2498 |
| 0.0761        | 17.01 | 17200 | 0.4159          | 0.2563 |
| 0.0711        | 17.41 | 17600 | 0.4112          | 0.2603 |
| 0.0698        | 17.8  | 18000 | 0.4335          | 0.2529 |
| 0.073         | 18.2  | 18400 | 0.4120          | 0.2512 |
| 0.0665        | 18.6  | 18800 | 0.4335          | 0.2496 |
| 0.0657        | 18.99 | 19200 | 0.4143          | 0.2468 |
| 0.0617        | 19.39 | 19600 | 0.4339          | 0.2435 |
| 0.06          | 19.78 | 20000 | 0.4179          | 0.2438 |
| 0.0613        | 20.18 | 20400 | 0.4251          | 0.2393 |
| 0.0583        | 20.57 | 20800 | 0.4347          | 0.2422 |
| 0.0562        | 20.97 | 21200 | 0.4246          | 0.2377 |
| 0.053         | 21.36 | 21600 | 0.4198          | 0.2338 |
| 0.0525        | 21.76 | 22000 | 0.4511          | 0.2427 |
| 0.0499        | 22.16 | 22400 | 0.4482          | 0.2353 |
| 0.0475        | 22.55 | 22800 | 0.4449          | 0.2329 |
| 0.0465        | 22.95 | 23200 | 0.4364          | 0.2320 |
| 0.0443        | 23.34 | 23600 | 0.4481          | 0.2304 |
| 0.0458        | 23.74 | 24000 | 0.4442          | 0.2267 |
| 0.0453        | 24.13 | 24400 | 0.4402          | 0.2261 |
| 0.0426        | 24.53 | 24800 | 0.4262          | 0.2232 |
| 0.0431        | 24.93 | 25200 | 0.4251          | 0.2210 |
| 0.0389        | 25.32 | 25600 | 0.4455          | 0.2232 |
| 0.039         | 25.72 | 26000 | 0.4372          | 0.2236 |
| 0.0378        | 26.11 | 26400 | 0.4236          | 0.2212 |
| 0.0348        | 26.51 | 26800 | 0.4359          | 0.2204 |
| 0.0361        | 26.9  | 27200 | 0.4248          | 0.2192 |
| 0.0356        | 27.3  | 27600 | 0.4397          | 0.2184 |
| 0.0325        | 27.7  | 28000 | 0.4367          | 0.2181 |
| 0.0313        | 28.09 | 28400 | 0.4477          | 0.2136 |
| 0.0306        | 28.49 | 28800 | 0.4533          | 0.2135 |
| 0.0314        | 28.88 | 29200 | 0.4410          | 0.2136 |
| 0.0307        | 29.28 | 29600 | 0.4457          | 0.2113 |
| 0.0309        | 29.67 | 30000 | 0.4426          | 0.2117 |


### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0