---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_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_13_0
      type: common_voice_13_0
      config: fy-NL
      split: validation
      args: fy-NL
    metrics:
    - name: Wer
      type: wer
      value: 0.1492598825428444
  - 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: 0.15356265356265356
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: fy-NL
      split: test
      args: fy-NL
    metrics:
    - name: Wer
      type: wer
      value: 0.14712316399874995
---

<!-- 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_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2204
- Wer: 0.1493

## 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: 7e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9606        | 2.45  | 300  | 2.6184          | 1.0    |
| 1.4992        | 4.9   | 600  | 0.4233          | 0.4143 |
| 0.9757        | 7.35  | 900  | 0.2765          | 0.3021 |
| 0.8773        | 9.8   | 1200 | 0.2529          | 0.2528 |
| 0.7448        | 12.24 | 1500 | 0.2363          | 0.2258 |
| 0.7039        | 14.69 | 1800 | 0.2258          | 0.2103 |
| 0.6811        | 17.14 | 2100 | 0.2217          | 0.2074 |
| 0.6279        | 19.59 | 2400 | 0.2050          | 0.1915 |
| 0.5938        | 22.04 | 2700 | 0.2229          | 0.1922 |
| 0.6227        | 24.49 | 3000 | 0.2088          | 0.2019 |
| 0.5682        | 26.94 | 3300 | 0.2127          | 0.1874 |
| 0.5939        | 29.39 | 3600 | 0.2044          | 0.1789 |
| 0.5427        | 31.84 | 3900 | 0.2185          | 0.1791 |
| 0.5551        | 34.41 | 4200 | 0.2097          | 0.1644 |
| 0.5021        | 36.86 | 4500 | 0.2180          | 0.1678 |
| 0.4589        | 39.31 | 4800 | 0.2076          | 0.1581 |
| 0.5204        | 41.76 | 5100 | 0.2181          | 0.1587 |
| 0.512         | 44.21 | 5400 | 0.2263          | 0.1607 |
| 0.465         | 46.66 | 5700 | 0.2204          | 0.1493 |


### Framework versions

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