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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-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: 0.07238251678331667
- 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: 0.07103627691862986
---
<!-- 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-300m-frisian-cv-8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_8_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0707
- Wer: 0.0724
And on the test set:
- Wer: 0.0710
## Model description
This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 6 where
I use as training set all validated data (~ 50 hours) except the test and evaluation sets (~ 4.5 hours each).
The number of training hours adds up to 41 hours of Frisian speech. This varies from experiment 2 because I fine-tune on the 300M/0.3B parameters version of XLS-R.
## 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 corresponds to all of the validated data except for the recordings found in the evaluation and test 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: 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: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 14.7268 | 0.43 | 400 | 8.7389 | 1.0 |
| 5.3377 | 0.86 | 800 | 3.7016 | 1.0 |
| 3.343 | 1.29 | 1200 | 3.0984 | 1.0 |
| 3.0306 | 1.71 | 1600 | 2.9643 | 1.0 |
| 2.9511 | 2.14 | 2000 | 2.9273 | 1.0 |
| 2.9078 | 2.57 | 2400 | 2.8202 | 1.0 |
| 2.4965 | 3.0 | 2800 | 1.3805 | 0.8888 |
| 1.5378 | 3.43 | 3200 | 0.6556 | 0.5720 |
| 1.119 | 3.86 | 3600 | 0.4260 | 0.4077 |
| 0.9159 | 4.29 | 4000 | 0.3457 | 0.3322 |
| 0.8037 | 4.72 | 4400 | 0.2765 | 0.2850 |
| 0.7411 | 5.14 | 4800 | 0.2447 | 0.2473 |
| 0.6767 | 5.57 | 5200 | 0.2176 | 0.2234 |
| 0.6296 | 6.0 | 5600 | 0.1996 | 0.2078 |
| 0.6165 | 6.43 | 6000 | 0.1891 | 0.1977 |
| 0.5856 | 6.86 | 6400 | 0.1763 | 0.1855 |
| 0.5674 | 7.29 | 6800 | 0.1708 | 0.1797 |
| 0.5399 | 7.72 | 7200 | 0.1593 | 0.1694 |
| 0.5195 | 8.15 | 7600 | 0.1551 | 0.1660 |
| 0.4973 | 8.57 | 8000 | 0.1509 | 0.1583 |
| 0.4907 | 9.0 | 8400 | 0.1480 | 0.1525 |
| 0.4681 | 9.43 | 8800 | 0.1389 | 0.1494 |
| 0.4513 | 9.86 | 9200 | 0.1368 | 0.1414 |
| 0.4486 | 10.29 | 9600 | 0.1294 | 0.1390 |
| 0.4381 | 10.72 | 10000 | 0.1262 | 0.1354 |
| 0.443 | 11.15 | 10400 | 0.1234 | 0.1313 |
| 0.4182 | 11.58 | 10800 | 0.1196 | 0.1294 |
| 0.4036 | 12.0 | 11200 | 0.1194 | 0.1259 |
| 0.4027 | 12.43 | 11600 | 0.1170 | 0.1226 |
| 0.4066 | 12.86 | 12000 | 0.1156 | 0.1224 |
| 0.3885 | 13.29 | 12400 | 0.1136 | 0.1174 |
| 0.3859 | 13.72 | 12800 | 0.1121 | 0.1146 |
| 0.3812 | 14.15 | 13200 | 0.1097 | 0.1141 |
| 0.3774 | 14.58 | 13600 | 0.1059 | 0.1130 |
| 0.3678 | 15.01 | 14000 | 0.1058 | 0.1096 |
| 0.3586 | 15.43 | 14400 | 0.1026 | 0.1099 |
| 0.3612 | 15.86 | 14800 | 0.1010 | 0.1076 |
| 0.3626 | 16.29 | 15200 | 0.0993 | 0.1068 |
| 0.353 | 16.72 | 15600 | 0.0974 | 0.1046 |
| 0.3564 | 17.15 | 16000 | 0.0986 | 0.1037 |
| 0.3447 | 17.58 | 16400 | 0.0977 | 0.1041 |
| 0.3454 | 18.01 | 16800 | 0.0945 | 0.1023 |
| 0.3338 | 18.44 | 17200 | 0.0904 | 0.0996 |
| 0.3359 | 18.86 | 17600 | 0.0950 | 0.1002 |
| 0.3179 | 19.29 | 18000 | 0.0911 | 0.0977 |
| 0.3202 | 19.72 | 18400 | 0.0906 | 0.0979 |
| 0.3317 | 20.15 | 18800 | 0.0894 | 0.0963 |
| 0.3187 | 20.58 | 19200 | 0.0878 | 0.0938 |
| 0.3075 | 21.01 | 19600 | 0.0893 | 0.0937 |
| 0.3032 | 21.44 | 20000 | 0.0872 | 0.0923 |
| 0.3048 | 21.86 | 20400 | 0.0848 | 0.0921 |
| 0.3045 | 22.29 | 20800 | 0.0860 | 0.0887 |
| 0.316 | 22.72 | 21200 | 0.0841 | 0.0896 |
| 0.2986 | 23.15 | 21600 | 0.0840 | 0.0876 |
| 0.294 | 23.58 | 22000 | 0.0824 | 0.0862 |
| 0.313 | 24.01 | 22400 | 0.0814 | 0.0855 |
| 0.2864 | 24.44 | 22800 | 0.0816 | 0.0861 |
| 0.2927 | 24.87 | 23200 | 0.0807 | 0.0875 |
| 0.294 | 25.29 | 23600 | 0.0829 | 0.0826 |
| 0.2834 | 25.72 | 24000 | 0.0794 | 0.0823 |
| 0.2852 | 26.15 | 24400 | 0.0781 | 0.0815 |
| 0.2823 | 26.58 | 24800 | 0.0781 | 0.0821 |
| 0.2835 | 27.01 | 25200 | 0.0788 | 0.0826 |
| 0.2763 | 27.44 | 25600 | 0.0789 | 0.0823 |
| 0.2845 | 27.87 | 26000 | 0.0767 | 0.0803 |
| 0.2777 | 28.3 | 26400 | 0.0775 | 0.0809 |
| 0.275 | 28.72 | 26800 | 0.0758 | 0.0794 |
| 0.2707 | 29.15 | 27200 | 0.0745 | 0.0790 |
| 0.2734 | 29.58 | 27600 | 0.0765 | 0.0797 |
| 0.2716 | 30.01 | 28000 | 0.0746 | 0.0780 |
| 0.2626 | 30.44 | 28400 | 0.0756 | 0.0776 |
| 0.2671 | 30.87 | 28800 | 0.0742 | 0.0763 |
| 0.2592 | 31.3 | 29200 | 0.0730 | 0.0771 |
| 0.2685 | 31.73 | 29600 | 0.0733 | 0.0760 |
| 0.2727 | 32.15 | 30000 | 0.0738 | 0.0758 |
| 0.2564 | 32.58 | 30400 | 0.0731 | 0.0763 |
| 0.2528 | 33.01 | 30800 | 0.0730 | 0.0758 |
| 0.2573 | 33.44 | 31200 | 0.0717 | 0.0746 |
| 0.2597 | 33.87 | 31600 | 0.0718 | 0.0760 |
| 0.2511 | 34.3 | 32000 | 0.0737 | 0.0750 |
| 0.2551 | 34.73 | 32400 | 0.0732 | 0.0758 |
| 0.26 | 35.16 | 32800 | 0.0724 | 0.0746 |
| 0.2563 | 35.58 | 33200 | 0.0717 | 0.0730 |
| 0.2559 | 36.01 | 33600 | 0.0707 | 0.0734 |
| 0.2499 | 36.44 | 34000 | 0.0721 | 0.0729 |
| 0.252 | 36.87 | 34400 | 0.0716 | 0.0723 |
| 0.2448 | 37.3 | 34800 | 0.0711 | 0.0725 |
| 0.248 | 37.73 | 35200 | 0.0710 | 0.0727 |
| 0.2568 | 38.16 | 35600 | 0.0710 | 0.0720 |
| 0.2471 | 38.59 | 36000 | 0.0707 | 0.0725 |
| 0.2464 | 39.01 | 36400 | 0.0705 | 0.0719 |
| 0.2477 | 39.44 | 36800 | 0.0706 | 0.0727 |
| 0.2482 | 39.87 | 37200 | 0.0707 | 0.0724 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
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