---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_12_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-1b-frisian
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_12_0
      type: common_voice_12_0
      config: fy-NL
      split: validation
      args: fy-NL
    metrics:
    - name: Wer
      type: wer
      value: 0.1685917915949865
---

<!-- 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

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_12_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2748
- Wer: 0.1686

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.6809        | 2.1   | 250  | 2.0948          | 0.9948 |
| 1.2928        | 4.2   | 500  | 0.4505          | 0.4003 |
| 0.7887        | 6.3   | 750  | 0.3410          | 0.3287 |
| 0.7422        | 8.4   | 1000 | 0.3017          | 0.2756 |
| 0.7277        | 10.5  | 1250 | 0.3014          | 0.2624 |
| 0.6339        | 12.61 | 1500 | 0.2833          | 0.2398 |
| 0.5284        | 14.71 | 1750 | 0.2970          | 0.2404 |
| 0.5186        | 16.81 | 2000 | 0.2886          | 0.2400 |
| 0.515         | 18.91 | 2250 | 0.2891          | 0.2335 |
| 0.5199        | 21.01 | 2500 | 0.2985          | 0.2261 |
| 0.5228        | 23.11 | 2750 | 0.3026          | 0.2187 |
| 0.5102        | 25.21 | 3000 | 0.2829          | 0.1994 |
| 0.463         | 27.31 | 3250 | 0.2885          | 0.2012 |
| 0.5072        | 29.41 | 3500 | 0.2936          | 0.1971 |
| 0.4581        | 31.51 | 3750 | 0.2979          | 0.1912 |
| 0.4103        | 33.61 | 4000 | 0.2935          | 0.1875 |
| 0.3414        | 35.71 | 4250 | 0.2999          | 0.1860 |
| 0.4484        | 37.82 | 4500 | 0.2917          | 0.1810 |
| 0.3523        | 39.92 | 4750 | 0.2875          | 0.1759 |
| 0.3763        | 42.02 | 5000 | 0.2901          | 0.1758 |
| 0.2416        | 44.12 | 5250 | 0.2707          | 0.1740 |
| 0.1878        | 46.22 | 5500 | 0.2707          | 0.1717 |
| 0.1623        | 48.32 | 5750 | 0.2748          | 0.1686 |


### Framework versions

- Transformers 4.27.3
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2