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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-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
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