---
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: 0.14290815597771747
  - 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: 0.1413499060557884
---

<!-- 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: 0.2131
- Wer: 0.1429

And on the test set:
- Wer: 0.1413

## Model description

This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 1 where 
I use the same training set as the XLSR-53 baseline.

## 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 training and evaluation splits used are the ones available in the Common Voice 8.0 Frisian subset.

## 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: 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.0565        | 1.72  | 200  | 3.1053          | 1.0    |
| 2.7675        | 3.45  | 400  | 1.1551          | 0.8611 |
| 1.3474        | 5.17  | 600  | 0.4770          | 0.4397 |
| 0.9617        | 6.9   | 800  | 0.3218          | 0.3343 |
| 0.9058        | 8.62  | 1000 | 0.2741          | 0.2768 |
| 0.9712        | 10.34 | 1200 | 0.2619          | 0.2505 |
| 0.6908        | 12.07 | 1400 | 0.2288          | 0.2243 |
| 0.745         | 13.79 | 1600 | 0.2288          | 0.2095 |
| 0.7742        | 15.52 | 1800 | 0.2289          | 0.1979 |
| 0.7231        | 17.24 | 2000 | 0.2198          | 0.1940 |
| 0.6475        | 18.97 | 2200 | 0.2180          | 0.1992 |
| 0.6421        | 20.69 | 2400 | 0.2133          | 0.1741 |
| 0.5925        | 22.41 | 2600 | 0.1998          | 0.1747 |
| 0.5608        | 24.14 | 2800 | 0.2212          | 0.1950 |
| 0.5315        | 25.86 | 3000 | 0.2187          | 0.1624 |
| 0.5362        | 27.59 | 3200 | 0.2057          | 0.1718 |
| 0.563         | 29.31 | 3400 | 0.2090          | 0.1613 |
| 0.4218        | 31.03 | 3600 | 0.2126          | 0.1531 |
| 0.3826        | 32.76 | 3800 | 0.2084          | 0.1538 |
| 0.356         | 34.48 | 4000 | 0.2115          | 0.1612 |
| 0.2966        | 36.21 | 4200 | 0.2093          | 0.1536 |
| 0.3377        | 37.93 | 4400 | 0.2061          | 0.1527 |
| 0.321         | 39.66 | 4600 | 0.2121          | 0.1463 |
| 0.2942        | 41.38 | 4800 | 0.2158          | 0.1441 |
| 0.2931        | 43.1  | 5000 | 0.2173          | 0.1446 |
| 0.2346        | 44.83 | 5200 | 0.2152          | 0.1436 |
| 0.2543        | 46.55 | 5400 | 0.2066          | 0.1445 |
| 0.2385        | 48.28 | 5600 | 0.2108          | 0.1432 |
| 0.2726        | 50.0  | 5800 | 0.2131          | 0.1429 |



### Framework versions

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