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---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
base_model: nguyenvulebinh/wav2vec2-base-vietnamese-250h
model-index:
- name: model_weight
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: vi
split: None
args: vi
metrics:
- type: wer
value: 0.14013683555810727
name: Wer
---
<!-- 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
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: 0.1765
- Wer: 0.1401
## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 15.0719 | 1.3928 | 500 | 4.8260 | 1.0 |
| 4.4273 | 2.7855 | 1000 | 4.6865 | 0.9991 |
| 3.9296 | 4.1783 | 1500 | 4.2965 | 0.9992 |
| 3.4964 | 5.5710 | 2000 | 2.6642 | 0.9583 |
| 2.8184 | 6.9638 | 2500 | 1.7146 | 0.8718 |
| 2.132 | 8.3565 | 3000 | 1.4549 | 0.7103 |
| 1.7481 | 9.7493 | 3500 | 0.9072 | 0.5730 |
| 1.5776 | 11.1421 | 4000 | 0.7414 | 0.5132 |
| 1.3743 | 12.5348 | 4500 | 0.6621 | 0.4089 |
| 1.2417 | 13.9276 | 5000 | 0.4884 | 0.3854 |
| 1.1375 | 15.3203 | 5500 | 0.3561 | 0.3123 |
| 1.0412 | 16.7131 | 6000 | 0.3344 | 0.2945 |
| 0.981 | 18.1058 | 6500 | 0.3063 | 0.2667 |
| 0.9913 | 19.4986 | 7000 | 0.2778 | 0.2244 |
| 0.861 | 20.8914 | 7500 | 0.2511 | 0.2170 |
| 0.8314 | 22.2841 | 8000 | 0.2498 | 0.2127 |
| 0.8669 | 23.6769 | 8500 | 0.2452 | 0.2048 |
| 0.8003 | 25.0696 | 9000 | 0.2251 | 0.1830 |
| 0.7409 | 26.4624 | 9500 | 0.2292 | 0.1820 |
| 0.7282 | 27.8552 | 10000 | 0.2130 | 0.1681 |
| 0.7675 | 29.2479 | 10500 | 0.2290 | 0.1796 |
| 0.7295 | 30.6407 | 11000 | 0.1971 | 0.1617 |
| 0.6308 | 32.0334 | 11500 | 0.2032 | 0.1555 |
| 0.6251 | 33.4262 | 12000 | 0.1905 | 0.1515 |
| 0.5887 | 34.8189 | 12500 | 0.1844 | 0.1481 |
| 0.6642 | 36.2117 | 13000 | 0.1796 | 0.1444 |
| 0.6068 | 37.6045 | 13500 | 0.1808 | 0.1417 |
| 0.5862 | 38.9972 | 14000 | 0.1765 | 0.1401 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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