---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-1b-frisian-cv-8-10h
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_8_0
      type: common_voice_8_0
      config: fy-NL
      split: validation
      args: fy-NL
    metrics:
    - name: Wer
      type: wer
      value: 0.09612912441079846
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_8_0
      type: common_voice_8_0
      config: fy-NL
      split: test
      args: fy-NL
    metrics:
    - name: Wer
      type: wer
      value: 0.08830755889579418
---

<!-- 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-1b-frisian-cv-8-10h

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice_8_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1207
- Wer: 0.0961

And on the test set:
- Wer: 0.0883

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.6342        | 1.32  | 300   | 2.9760          | 1.0    |
| 2.2716        | 2.63  | 600   | 0.6877          | 0.6024 |
| 1.1303        | 3.95  | 900   | 0.3522          | 0.3450 |
| 0.9038        | 5.26  | 1200  | 0.2714          | 0.2603 |
| 0.846         | 6.58  | 1500  | 0.2143          | 0.2036 |
| 0.8044        | 7.89  | 1800  | 0.1829          | 0.1788 |
| 0.7069        | 9.21  | 2100  | 0.1751          | 0.1667 |
| 0.6995        | 10.53 | 2400  | 0.1741          | 0.1727 |
| 0.7115        | 11.84 | 2700  | 0.1591          | 0.1486 |
| 0.677         | 13.16 | 3000  | 0.1636          | 0.1459 |
| 0.6032        | 14.47 | 3300  | 0.1535          | 0.1439 |
| 0.6218        | 15.79 | 3600  | 0.1427          | 0.1406 |
| 0.6519        | 17.11 | 3900  | 0.1498          | 0.1488 |
| 0.5739        | 18.42 | 4200  | 0.1438          | 0.1319 |
| 0.567         | 19.74 | 4500  | 0.1379          | 0.1322 |
| 0.4982        | 21.05 | 4800  | 0.1315          | 0.1237 |
| 0.5825        | 22.37 | 5100  | 0.1349          | 0.1252 |
| 0.5085        | 23.68 | 5400  | 0.1297          | 0.1233 |
| 0.4946        | 25.0  | 5700  | 0.1343          | 0.1127 |
| 0.5677        | 26.32 | 6000  | 0.1323          | 0.1228 |
| 0.4858        | 27.63 | 6300  | 0.1292          | 0.1098 |
| 0.4709        | 28.95 | 6600  | 0.1267          | 0.1204 |
| 0.3241        | 30.26 | 6900  | 0.1315          | 0.1274 |
| 0.2796        | 31.58 | 7200  | 0.1315          | 0.1202 |
| 0.3171        | 32.89 | 7500  | 0.1315          | 0.1200 |
| 0.2591        | 34.21 | 7800  | 0.1322          | 0.1106 |
| 0.2716        | 35.53 | 8100  | 0.1233          | 0.1030 |
| 0.2446        | 36.84 | 8400  | 0.1273          | 0.1087 |
| 0.2377        | 38.16 | 8700  | 0.1243          | 0.1101 |
| 0.2183        | 39.47 | 9000  | 0.1230          | 0.1116 |
| 0.2059        | 40.79 | 9300  | 0.1240          | 0.1001 |
| 0.1916        | 42.11 | 9600  | 0.1223          | 0.1003 |
| 0.196         | 43.42 | 9900  | 0.1246          | 0.0965 |
| 0.1969        | 44.74 | 10200 | 0.1222          | 0.1038 |
| 0.1951        | 46.05 | 10500 | 0.1208          | 0.1003 |
| 0.1809        | 47.37 | 10800 | 0.1213          | 0.1003 |
| 0.1793        | 48.68 | 11100 | 0.1202          | 0.0959 |
| 0.1837        | 50.0  | 11400 | 0.1207          | 0.0961 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3