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---
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
|