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---
license: cc-by-nc-4.0
base_model: nguyenvulebinh/wav2vec2-base-vietnamese-250h
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: model_weight_with_token_110_11
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: vi
split: None
args: vi
metrics:
- name: Wer
type: wer
value: 0.1738111185532485
---
<!-- 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. -->
# model_weight_with_token_110_11
This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0692
- Cer: 0.0582
- Wer: 0.1738
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 0.5359 | 1.3928 | 500 | 0.1281 | 0.0733 | 0.2101 |
| 0.4994 | 2.7855 | 1000 | 0.1591 | 0.0731 | 0.2098 |
| 0.4735 | 4.1783 | 1500 | 0.1169 | 0.0708 | 0.2022 |
| 0.4432 | 5.5710 | 2000 | 0.1102 | 0.0710 | 0.2031 |
| 0.4204 | 6.9638 | 2500 | 0.1127 | 0.0735 | 0.2023 |
| 0.3919 | 8.3565 | 3000 | 0.1146 | 0.0699 | 0.1992 |
| 0.3701 | 9.7493 | 3500 | 0.1047 | 0.0712 | 0.1993 |
| 0.3826 | 11.1421 | 4000 | 0.1073 | 0.0688 | 0.1952 |
| 0.3512 | 12.5348 | 4500 | 0.0898 | 0.0650 | 0.1894 |
| 0.327 | 13.9276 | 5000 | 0.0931 | 0.0650 | 0.1869 |
| 0.3284 | 15.3203 | 5500 | 0.1028 | 0.0672 | 0.1918 |
| 0.3016 | 16.7131 | 6000 | 0.0973 | 0.0651 | 0.1875 |
| 0.2893 | 18.1058 | 6500 | 0.0899 | 0.0638 | 0.1849 |
| 0.292 | 19.4986 | 7000 | 0.0882 | 0.0632 | 0.1840 |
| 0.2688 | 20.8914 | 7500 | 0.0808 | 0.0625 | 0.1820 |
| 0.2672 | 22.2841 | 8000 | 0.0807 | 0.0620 | 0.1818 |
| 0.266 | 23.6769 | 8500 | 0.0807 | 0.0621 | 0.1816 |
| 0.2499 | 25.0696 | 9000 | 0.0741 | 0.0615 | 0.1787 |
| 0.244 | 26.4624 | 9500 | 0.0780 | 0.0620 | 0.1803 |
| 0.2403 | 27.8552 | 10000 | 0.0764 | 0.0607 | 0.1791 |
| 0.2268 | 29.2479 | 10500 | 0.0750 | 0.0599 | 0.1778 |
| 0.2318 | 30.6407 | 11000 | 0.0728 | 0.0600 | 0.1775 |
| 0.2143 | 32.0334 | 11500 | 0.0723 | 0.0592 | 0.1757 |
| 0.2193 | 33.4262 | 12000 | 0.0699 | 0.0591 | 0.1749 |
| 0.1992 | 34.8189 | 12500 | 0.0717 | 0.0585 | 0.1748 |
| 0.2238 | 36.2117 | 13000 | 0.0692 | 0.0587 | 0.1744 |
| 0.2074 | 37.6045 | 13500 | 0.0696 | 0.0581 | 0.1735 |
| 0.1984 | 38.9972 | 14000 | 0.0692 | 0.0582 | 0.1738 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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