---
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-8-1h
  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: 0.23732323953720896
  - 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: 0.25404682757623936
---

<!-- 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-8-1h

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: 0.4120
- Wer: 0.2373

And on the test set:
- Wer: 0.2540

## Model description

This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 4 where 
I use as training set 1 hour of Frisian speech randomly selected from all validated data except the test and evaluation sets.

## Intended uses & limitations

The intended use is for recognizing Frisian speech.

Limitations include no LM rescoring and using version 8.0 of Common Voice instead of 13.0.

## Training and evaluation data

The evaluation split used is the one available in the Common Voice 8.0 Frisian subset. The train split is 1 hour of Frisian randomly selected from validated data except for the recordings from test and evaluation splits.

## Training procedure

The script used for training this model can be found in this GitHub repository: [link](https://github.com/greenw0lf/MSc-VT-Thesis/).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.2987        | 4.35  | 100  | 3.0210          | 1.0    |
| 3.1424        | 8.7   | 200  | 2.9611          | 1.0    |
| 2.6299        | 13.04 | 300  | 0.9929          | 0.8377 |
| 1.3134        | 17.39 | 400  | 0.5679          | 0.5264 |
| 0.9747        | 21.74 | 500  | 0.4516          | 0.3764 |
| 0.8755        | 26.09 | 600  | 0.4515          | 0.3403 |
| 0.7227        | 30.43 | 700  | 0.4169          | 0.3211 |
| 0.6634        | 34.78 | 800  | 0.4159          | 0.2962 |
| 0.5568        | 39.13 | 900  | 0.4081          | 0.2795 |
| 0.7943        | 43.48 | 1000 | 0.4090          | 0.2709 |
| 0.5537        | 47.83 | 1100 | 0.4239          | 0.2649 |
| 0.5596        | 52.17 | 1200 | 0.4029          | 0.2561 |
| 0.5523        | 56.52 | 1300 | 0.4073          | 0.2524 |
| 0.4579        | 60.87 | 1400 | 0.4098          | 0.2470 |
| 0.6477        | 65.22 | 1500 | 0.4099          | 0.2446 |
| 0.4957        | 69.57 | 1600 | 0.4167          | 0.2475 |
| 0.3246        | 73.91 | 1700 | 0.4146          | 0.2389 |
| 0.3937        | 78.26 | 1800 | 0.4120          | 0.2373 |


### Framework versions

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