---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-finetune-vi-v4
  results: []
widget:
  - example_title: SOICT 2023 - SLU public test 1
    src: >-
      https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi-v4/raw/main/audio-test/BA1LQuAP7SlUPuOr8kIQ6Y4.wav
  - example_title: SOICT 2023 - SLU public test 2
    src: >-
      https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi-v4/raw/main/audio-test/lbzKPYnMDWaBeGBvSp7ngxd.wav
  - example_title: SOICT 2023 - SLU public test 3
    src: >-
      https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi-v4/raw/main/audio-test/ZwOTZLm5YSfjMDN3AEEoWvR.wav
---

<!-- 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-base-finetune-vi-v4

This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2829
- Wer: 0.1587

## 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: 8
- 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: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.0547        | 0.49  | 500   | 3.4652          | 1.0    |
| 2.9133        | 0.99  | 1000  | 1.1003          | 0.5035 |
| 1.0644        | 1.48  | 1500  | 0.5960          | 0.3199 |
| 0.8294        | 1.98  | 2000  | 0.5098          | 0.2809 |
| 0.6965        | 2.47  | 2500  | 0.5010          | 0.2596 |
| 0.6646        | 2.96  | 3000  | 0.4209          | 0.2398 |
| 0.5753        | 3.46  | 3500  | 0.4089          | 0.2361 |
| 0.5265        | 3.95  | 4000  | 0.3868          | 0.2195 |
| 0.4701        | 4.45  | 4500  | 0.3626          | 0.2171 |
| 0.4617        | 4.94  | 5000  | 0.3693          | 0.2160 |
| 0.4343        | 5.43  | 5500  | 0.3661          | 0.2058 |
| 0.4246        | 5.93  | 6000  | 0.3618          | 0.2067 |
| 0.3881        | 6.42  | 6500  | 0.3654          | 0.2044 |
| 0.3948        | 6.92  | 7000  | 0.3586          | 0.2009 |
| 0.367         | 7.41  | 7500  | 0.3431          | 0.1961 |
| 0.3449        | 7.91  | 8000  | 0.3196          | 0.1944 |
| 0.3168        | 8.4   | 8500  | 0.3310          | 0.1912 |
| 0.3393        | 8.89  | 9000  | 0.3418          | 0.1879 |
| 0.3197        | 9.39  | 9500  | 0.3434          | 0.1888 |
| 0.2954        | 9.88  | 10000 | 0.3371          | 0.1863 |
| 0.2968        | 10.38 | 10500 | 0.2941          | 0.1899 |
| 0.2802        | 10.87 | 11000 | 0.3095          | 0.1836 |
| 0.2783        | 11.36 | 11500 | 0.3275          | 0.1822 |
| 0.3027        | 11.86 | 12000 | 0.3103          | 0.1806 |
| 0.2645        | 12.35 | 12500 | 0.3247          | 0.1842 |
| 0.2958        | 12.85 | 13000 | 0.3242          | 0.1801 |
| 0.2648        | 13.34 | 13500 | 0.3169          | 0.1775 |
| 0.2461        | 13.83 | 14000 | 0.2926          | 0.1764 |
| 0.247         | 14.33 | 14500 | 0.3033          | 0.1741 |
| 0.2212        | 14.82 | 15000 | 0.2901          | 0.1749 |
| 0.2239        | 15.32 | 15500 | 0.3237          | 0.1758 |
| 0.2093        | 15.81 | 16000 | 0.2972          | 0.1759 |
| 0.2284        | 16.3  | 16500 | 0.3025          | 0.1749 |
| 0.228         | 16.8  | 17000 | 0.2862          | 0.1708 |
| 0.2033        | 17.29 | 17500 | 0.3039          | 0.1745 |
| 0.189         | 17.79 | 18000 | 0.3084          | 0.1708 |
| 0.1992        | 18.28 | 18500 | 0.2931          | 0.1735 |
| 0.1989        | 18.77 | 19000 | 0.2964          | 0.1693 |
| 0.1953        | 19.27 | 19500 | 0.3082          | 0.1715 |
| 0.1813        | 19.76 | 20000 | 0.2859          | 0.1702 |
| 0.1703        | 20.26 | 20500 | 0.2936          | 0.1680 |
| 0.1939        | 20.75 | 21000 | 0.2871          | 0.1684 |
| 0.1769        | 21.25 | 21500 | 0.2994          | 0.1646 |
| 0.1795        | 21.74 | 22000 | 0.2990          | 0.1669 |
| 0.17          | 22.23 | 22500 | 0.2839          | 0.1663 |
| 0.1507        | 22.73 | 23000 | 0.3125          | 0.1666 |
| 0.1676        | 23.22 | 23500 | 0.2867          | 0.1611 |
| 0.1675        | 23.72 | 24000 | 0.3099          | 0.1607 |
| 0.171         | 24.21 | 24500 | 0.3000          | 0.1627 |
| 0.1483        | 24.7  | 25000 | 0.3010          | 0.1629 |
| 0.1452        | 25.2  | 25500 | 0.2910          | 0.1641 |
| 0.1394        | 25.69 | 26000 | 0.2878          | 0.1605 |
| 0.1478        | 26.19 | 26500 | 0.2881          | 0.1617 |
| 0.1426        | 26.68 | 27000 | 0.2714          | 0.1607 |
| 0.1342        | 27.17 | 27500 | 0.2941          | 0.1615 |
| 0.1385        | 27.67 | 28000 | 0.2758          | 0.1594 |
| 0.1541        | 28.16 | 28500 | 0.2830          | 0.1592 |
| 0.153         | 28.66 | 29000 | 0.2789          | 0.1575 |
| 0.1359        | 29.15 | 29500 | 0.2819          | 0.1588 |
| 0.1276        | 29.64 | 30000 | 0.2829          | 0.1587 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.8.0
- Tokenizers 0.13.3