---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-finetune-vi-v3
  results: []
widget:
  - example_title: SOICT 2023 - SLU public test 1
    src: >-
      https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/055R7BruAa333g9teFfamQH.wav
  - example_title: SOICT 2023 - SLU public test 2
    src: >-
      https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/0BLHhoJexE8THB8BrsZxWbh.wav
  - example_title: SOICT 2023 - SLU public test 3
    src: >-
      https://huggingface.co/foxxy-hm/wav2vec2-base-finetune-vi/raw/main/audio-test/1ArUTGWJQ9YALH2xaNhU6GV.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-v3

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: 0.2019
- Wer: 0.1347

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.3444        | 0.59  | 500   | 3.0138          | 0.9938 |
| 1.6923        | 1.19  | 1000  | 0.6549          | 0.3873 |
| 0.8371        | 1.78  | 1500  | 0.3958          | 0.2492 |
| 0.6161        | 2.37  | 2000  | 0.3272          | 0.2161 |
| 0.5443        | 2.97  | 2500  | 0.2871          | 0.1967 |
| 0.4361        | 3.56  | 3000  | 0.2928          | 0.1852 |
| 0.3889        | 4.15  | 3500  | 0.2957          | 0.1745 |
| 0.3701        | 4.74  | 4000  | 0.2611          | 0.1702 |
| 0.353         | 5.34  | 4500  | 0.2514          | 0.1685 |
| 0.3299        | 5.93  | 5000  | 0.2239          | 0.1612 |
| 0.3235        | 6.52  | 5500  | 0.2362          | 0.1673 |
| 0.3           | 7.12  | 6000  | 0.2267          | 0.1593 |
| 0.285         | 7.71  | 6500  | 0.2397          | 0.1570 |
| 0.2766        | 8.3   | 7000  | 0.2415          | 0.1538 |
| 0.2652        | 8.9   | 7500  | 0.2265          | 0.1552 |
| 0.2396        | 9.49  | 8000  | 0.2299          | 0.1516 |
| 0.2442        | 10.08 | 8500  | 0.2136          | 0.1538 |
| 0.242         | 10.68 | 9000  | 0.2120          | 0.1510 |
| 0.2149        | 11.27 | 9500  | 0.2250          | 0.1494 |
| 0.2186        | 11.86 | 10000 | 0.2077          | 0.1491 |
| 0.2157        | 12.46 | 10500 | 0.2198          | 0.1461 |
| 0.2154        | 13.05 | 11000 | 0.2048          | 0.1484 |
| 0.2115        | 13.64 | 11500 | 0.2209          | 0.1444 |
| 0.2072        | 14.23 | 12000 | 0.2014          | 0.1414 |
| 0.1974        | 14.83 | 12500 | 0.2256          | 0.1451 |
| 0.1888        | 15.42 | 13000 | 0.1931          | 0.1445 |
| 0.1802        | 16.01 | 13500 | 0.2089          | 0.1452 |
| 0.1747        | 16.61 | 14000 | 0.2136          | 0.1441 |
| 0.1585        | 17.2  | 14500 | 0.2102          | 0.1411 |
| 0.1593        | 17.79 | 15000 | 0.1941          | 0.1413 |
| 0.1662        | 18.39 | 15500 | 0.2046          | 0.1426 |
| 0.165         | 18.98 | 16000 | 0.2077          | 0.1432 |
| 0.1696        | 19.57 | 16500 | 0.2062          | 0.1406 |
| 0.1499        | 20.17 | 17000 | 0.1997          | 0.1406 |
| 0.1459        | 20.76 | 17500 | 0.2063          | 0.1418 |
| 0.1381        | 21.35 | 18000 | 0.2040          | 0.1374 |
| 0.1504        | 21.95 | 18500 | 0.2013          | 0.1387 |
| 0.14          | 22.54 | 19000 | 0.2030          | 0.1393 |
| 0.1395        | 23.13 | 19500 | 0.2019          | 0.1357 |
| 0.1265        | 23.72 | 20000 | 0.2023          | 0.1364 |
| 0.1425        | 24.32 | 20500 | 0.1995          | 0.1362 |
| 0.1196        | 24.91 | 21000 | 0.2033          | 0.1368 |
| 0.1251        | 25.5  | 21500 | 0.2013          | 0.1357 |
| 0.1175        | 26.1  | 22000 | 0.2056          | 0.1360 |
| 0.1278        | 26.69 | 22500 | 0.2026          | 0.1353 |
| 0.1223        | 27.28 | 23000 | 0.1985          | 0.1365 |
| 0.1195        | 27.88 | 23500 | 0.2004          | 0.1358 |
| 0.1167        | 28.47 | 24000 | 0.1956          | 0.1364 |
| 0.1167        | 29.06 | 24500 | 0.2004          | 0.1350 |
| 0.1143        | 29.66 | 25000 | 0.2019          | 0.1347 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3