---
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
  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: 0.1339808598771604
---

<!-- 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

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: 0.2054
- Wer: 0.1340

## 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: 3e-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: 80
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0483        | 1.72  | 200  | 3.0438          | 1.0    |
| 2.6284        | 3.45  | 400  | 1.1501          | 0.9229 |
| 1.4359        | 5.17  | 600  | 0.5618          | 0.5329 |
| 1.1366        | 6.9   | 800  | 0.3899          | 0.3845 |
| 0.988         | 8.62  | 1000 | 0.3370          | 0.3302 |
| 0.8377        | 10.34 | 1200 | 0.2765          | 0.2834 |
| 0.8001        | 12.07 | 1400 | 0.2750          | 0.2438 |
| 0.8678        | 13.79 | 1600 | 0.2258          | 0.2160 |
| 0.7023        | 15.52 | 1800 | 0.2260          | 0.2072 |
| 0.8111        | 17.24 | 2000 | 0.2223          | 0.2070 |
| 0.658         | 18.97 | 2200 | 0.2121          | 0.1834 |
| 0.6574        | 20.69 | 2400 | 0.2136          | 0.1812 |
| 0.7521        | 22.41 | 2600 | 0.2175          | 0.1775 |
| 0.6515        | 24.14 | 2800 | 0.2018          | 0.1718 |
| 0.6955        | 25.86 | 3000 | 0.2121          | 0.1863 |
| 0.6605        | 27.59 | 3200 | 0.2003          | 0.1607 |
| 0.5403        | 29.31 | 3400 | 0.2042          | 0.1668 |
| 0.5064        | 31.03 | 3600 | 0.2021          | 0.1616 |
| 0.6811        | 32.76 | 3800 | 0.2026          | 0.1668 |
| 0.6787        | 34.48 | 4000 | 0.2122          | 0.1613 |
| 0.5595        | 36.21 | 4200 | 0.2001          | 0.1547 |
| 0.5225        | 37.93 | 4400 | 0.1992          | 0.1615 |
| 0.5522        | 39.66 | 4600 | 0.2023          | 0.1603 |
| 0.5364        | 41.38 | 4800 | 0.1992          | 0.1531 |
| 0.5157        | 43.1  | 5000 | 0.2060          | 0.1550 |
| 0.4382        | 44.83 | 5200 | 0.1985          | 0.1427 |
| 0.3658        | 46.55 | 5400 | 0.1964          | 0.1427 |
| 0.5336        | 48.28 | 5600 | 0.2143          | 0.1471 |
| 0.5479        | 50.0  | 5800 | 0.1962          | 0.1402 |
| 0.5203        | 51.72 | 6000 | 0.2022          | 0.1418 |
| 0.363         | 53.45 | 6200 | 0.2103          | 0.1429 |
| 0.3828        | 55.17 | 6400 | 0.2070          | 0.1417 |
| 0.3875        | 56.9  | 6600 | 0.2070          | 0.1411 |
| 0.3433        | 58.62 | 6800 | 0.2049          | 0.1418 |
| 0.2826        | 60.34 | 7000 | 0.2047          | 0.1417 |
| 0.294         | 62.07 | 7200 | 0.2022          | 0.1369 |
| 0.2776        | 63.79 | 7400 | 0.2115          | 0.1365 |
| 0.3178        | 65.52 | 7600 | 0.2005          | 0.1377 |
| 0.2913        | 67.24 | 7800 | 0.2047          | 0.1355 |
| 0.2642        | 68.97 | 8000 | 0.2069          | 0.1338 |
| 0.255         | 70.69 | 8200 | 0.2041          | 0.1336 |
| 0.2746        | 72.41 | 8400 | 0.2064          | 0.1331 |
| 0.2485        | 74.14 | 8600 | 0.2068          | 0.1327 |
| 0.2741        | 75.86 | 8800 | 0.2073          | 0.1331 |
| 0.2223        | 77.59 | 9000 | 0.2055          | 0.1338 |
| 0.2327        | 79.31 | 9200 | 0.2054          | 0.1340 |


### Framework versions

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