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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
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: 0.17328485312410297
---
<!-- 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
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: 0.0688
- Wer: 0.1733
## 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 | Wer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 0.5366 | 1.3928 | 500 | 0.1234 | 0.2107 |
| 0.4976 | 2.7855 | 1000 | 0.1343 | 0.2133 |
| 0.4734 | 4.1783 | 1500 | 0.1109 | 0.2037 |
| 0.4449 | 5.5710 | 2000 | 0.1111 | 0.2061 |
| 0.4194 | 6.9638 | 2500 | 0.1096 | 0.2024 |
| 0.3941 | 8.3565 | 3000 | 0.1231 | 0.1969 |
| 0.3767 | 9.7493 | 3500 | 0.1059 | 0.2002 |
| 0.3853 | 11.1421 | 4000 | 0.0998 | 0.1930 |
| 0.3584 | 12.5348 | 4500 | 0.0892 | 0.1905 |
| 0.3291 | 13.9276 | 5000 | 0.0926 | 0.1899 |
| 0.3279 | 15.3203 | 5500 | 0.0879 | 0.1878 |
| 0.3014 | 16.7131 | 6000 | 0.0831 | 0.1851 |
| 0.2886 | 18.1058 | 6500 | 0.0814 | 0.1857 |
| 0.2949 | 19.4986 | 7000 | 0.0880 | 0.1854 |
| 0.2661 | 20.8914 | 7500 | 0.0782 | 0.1829 |
| 0.2676 | 22.2841 | 8000 | 0.0789 | 0.1806 |
| 0.2663 | 23.6769 | 8500 | 0.0787 | 0.1805 |
| 0.2461 | 25.0696 | 9000 | 0.0788 | 0.1793 |
| 0.2484 | 26.4624 | 9500 | 0.0755 | 0.1804 |
| 0.2452 | 27.8552 | 10000 | 0.0715 | 0.1773 |
| 0.2261 | 29.2479 | 10500 | 0.0705 | 0.1764 |
| 0.2311 | 30.6407 | 11000 | 0.0757 | 0.1770 |
| 0.2195 | 32.0334 | 11500 | 0.0714 | 0.1763 |
| 0.2208 | 33.4262 | 12000 | 0.0697 | 0.1752 |
| 0.2029 | 34.8189 | 12500 | 0.0673 | 0.1744 |
| 0.2228 | 36.2117 | 13000 | 0.0691 | 0.1739 |
| 0.2056 | 37.6045 | 13500 | 0.0678 | 0.1738 |
| 0.2017 | 38.9972 | 14000 | 0.0688 | 0.1733 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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