---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-euskera-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice
      type: common_voice
      config: eu
      split: test
      args: eu
    metrics:
    - name: Wer
      type: wer
      value: 0.17522160918337998
---

<!-- 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-euskera-colab

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: 0.1873
- Wer: 0.1752

## 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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.5827        | 0.76  | 600   | 0.3985          | 0.5778 |
| 0.2963        | 1.51  | 1200  | 0.2492          | 0.4142 |
| 0.2122        | 2.27  | 1800  | 0.2132          | 0.3133 |
| 0.1686        | 3.03  | 2400  | 0.2078          | 0.2851 |
| 0.1373        | 3.79  | 3000  | 0.1910          | 0.2750 |
| 0.1254        | 4.54  | 3600  | 0.1850          | 0.2619 |
| 0.1137        | 5.3   | 4200  | 0.1874          | 0.2503 |
| 0.1008        | 6.06  | 4800  | 0.1857          | 0.2554 |
| 0.0934        | 6.81  | 5400  | 0.1844          | 0.2404 |
| 0.0876        | 7.57  | 6000  | 0.2001          | 0.2375 |
| 0.0801        | 8.33  | 6600  | 0.2036          | 0.2512 |
| 0.0732        | 9.09  | 7200  | 0.1921          | 0.2301 |
| 0.069         | 9.84  | 7800  | 0.1821          | 0.2330 |
| 0.0628        | 10.6  | 8400  | 0.1915          | 0.2249 |
| 0.0619        | 11.36 | 9000  | 0.1881          | 0.2113 |
| 0.0549        | 12.11 | 9600  | 0.1920          | 0.2076 |
| 0.0524        | 12.87 | 10200 | 0.1901          | 0.2079 |
| 0.0492        | 13.63 | 10800 | 0.1767          | 0.2020 |
| 0.0445        | 14.38 | 11400 | 0.1852          | 0.1933 |
| 0.0427        | 15.14 | 12000 | 0.1995          | 0.1994 |
| 0.0398        | 15.9  | 12600 | 0.1922          | 0.1932 |
| 0.037         | 16.66 | 13200 | 0.1956          | 0.1920 |
| 0.0353        | 17.41 | 13800 | 0.1990          | 0.1909 |
| 0.0327        | 18.17 | 14400 | 0.1906          | 0.1784 |
| 0.0311        | 18.93 | 15000 | 0.1847          | 0.1765 |
| 0.0295        | 19.68 | 15600 | 0.1873          | 0.1752 |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3