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---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- nl
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-nl
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
args: nl
metrics:
- name: Test WER
type: wer
value: 17.17
- name: Test CER
type: cer
value: 5.13
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: nl
metrics:
- name: Test WER
type: wer
value: 35.76
- name: Test CER
type: cer
value: 13.99
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: nl
metrics:
- name: Test WER
type: wer
value: 37.19
---
<!-- 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-nl
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 dataset.
It achieves the following results on the test set:
- Loss: 0.3923
- Wer: 0.1748
## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.5787 | 0.89 | 400 | 0.6354 | 0.5643 |
| 0.3036 | 1.78 | 800 | 0.3690 | 0.3552 |
| 0.188 | 2.67 | 1200 | 0.3239 | 0.2958 |
| 0.1434 | 3.56 | 1600 | 0.3093 | 0.2515 |
| 0.1245 | 4.44 | 2000 | 0.3024 | 0.2433 |
| 0.1095 | 5.33 | 2400 | 0.3249 | 0.2643 |
| 0.0979 | 6.22 | 2800 | 0.3191 | 0.2281 |
| 0.0915 | 7.11 | 3200 | 0.3152 | 0.2216 |
| 0.0829 | 8.0 | 3600 | 0.3419 | 0.2218 |
| 0.0777 | 8.89 | 4000 | 0.3432 | 0.2132 |
| 0.073 | 9.78 | 4400 | 0.3223 | 0.2131 |
| 0.0688 | 10.67 | 4800 | 0.3094 | 0.2152 |
| 0.0647 | 11.56 | 5200 | 0.3411 | 0.2152 |
| 0.0639 | 12.44 | 5600 | 0.3762 | 0.2135 |
| 0.0599 | 13.33 | 6000 | 0.3790 | 0.2137 |
| 0.0572 | 14.22 | 6400 | 0.3693 | 0.2118 |
| 0.0563 | 15.11 | 6800 | 0.3495 | 0.2139 |
| 0.0521 | 16.0 | 7200 | 0.3800 | 0.2023 |
| 0.0508 | 16.89 | 7600 | 0.3678 | 0.2033 |
| 0.0513 | 17.78 | 8000 | 0.3845 | 0.1987 |
| 0.0476 | 18.67 | 8400 | 0.3511 | 0.2037 |
| 0.045 | 19.56 | 8800 | 0.3794 | 0.1994 |
| 0.044 | 20.44 | 9200 | 0.3525 | 0.2050 |
| 0.043 | 21.33 | 9600 | 0.4082 | 0.2007 |
| 0.0409 | 22.22 | 10000 | 0.3866 | 0.2004 |
| 0.0393 | 23.11 | 10400 | 0.3899 | 0.2008 |
| 0.0382 | 24.0 | 10800 | 0.3626 | 0.1951 |
| 0.039 | 24.89 | 11200 | 0.3936 | 0.1953 |
| 0.0361 | 25.78 | 11600 | 0.4262 | 0.1928 |
| 0.0362 | 26.67 | 12000 | 0.3796 | 0.1934 |
| 0.033 | 27.56 | 12400 | 0.3616 | 0.1934 |
| 0.0321 | 28.44 | 12800 | 0.3742 | 0.1933 |
| 0.0325 | 29.33 | 13200 | 0.3582 | 0.1869 |
| 0.0309 | 30.22 | 13600 | 0.3717 | 0.1874 |
| 0.029 | 31.11 | 14000 | 0.3814 | 0.1894 |
| 0.0296 | 32.0 | 14400 | 0.3698 | 0.1877 |
| 0.0281 | 32.89 | 14800 | 0.3976 | 0.1899 |
| 0.0275 | 33.78 | 15200 | 0.3854 | 0.1858 |
| 0.0264 | 34.67 | 15600 | 0.4021 | 0.1889 |
| 0.0261 | 35.56 | 16000 | 0.3850 | 0.1830 |
| 0.0242 | 36.44 | 16400 | 0.4091 | 0.1878 |
| 0.0245 | 37.33 | 16800 | 0.4012 | 0.1846 |
| 0.0243 | 38.22 | 17200 | 0.3996 | 0.1833 |
| 0.0223 | 39.11 | 17600 | 0.3962 | 0.1815 |
| 0.0223 | 40.0 | 18000 | 0.3898 | 0.1832 |
| 0.0219 | 40.89 | 18400 | 0.4019 | 0.1822 |
| 0.0211 | 41.78 | 18800 | 0.4035 | 0.1809 |
| 0.021 | 42.67 | 19200 | 0.3915 | 0.1826 |
| 0.0208 | 43.56 | 19600 | 0.3934 | 0.1784 |
| 0.0188 | 44.44 | 20000 | 0.3912 | 0.1787 |
| 0.0195 | 45.33 | 20400 | 0.3989 | 0.1766 |
| 0.0186 | 46.22 | 20800 | 0.3887 | 0.1773 |
| 0.0188 | 47.11 | 21200 | 0.3982 | 0.1758 |
| 0.0175 | 48.0 | 21600 | 0.3933 | 0.1755 |
| 0.0172 | 48.89 | 22000 | 0.3921 | 0.1749 |
| 0.0187 | 49.78 | 22400 | 0.3923 | 0.1748 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
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