---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-finetune-vi-v2
  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-v2

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: 0.2188
- Wer: 0.1391

## 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: 24

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.3873        | 0.67  | 500   | 2.4321          | 0.9719 |
| 1.4812        | 1.34  | 1000  | 0.5449          | 0.3062 |
| 0.7731        | 2.0   | 1500  | 0.3793          | 0.2263 |
| 0.542         | 2.67  | 2000  | 0.3021          | 0.2002 |
| 0.4461        | 3.34  | 2500  | 0.2905          | 0.1862 |
| 0.4175        | 4.01  | 3000  | 0.2687          | 0.1771 |
| 0.3878        | 4.67  | 3500  | 0.2958          | 0.1751 |
| 0.3373        | 5.34  | 4000  | 0.2713          | 0.1721 |
| 0.3046        | 6.01  | 4500  | 0.2505          | 0.1616 |
| 0.2933        | 6.68  | 5000  | 0.2561          | 0.1611 |
| 0.285         | 7.34  | 5500  | 0.2405          | 0.1617 |
| 0.2998        | 8.01  | 6000  | 0.2363          | 0.1578 |
| 0.2486        | 8.68  | 6500  | 0.2254          | 0.1570 |
| 0.2682        | 9.35  | 7000  | 0.2306          | 0.1547 |
| 0.2327        | 10.01 | 7500  | 0.2289          | 0.1537 |
| 0.2141        | 10.68 | 8000  | 0.2383          | 0.1499 |
| 0.2124        | 11.35 | 8500  | 0.2261          | 0.15   |
| 0.2156        | 12.02 | 9000  | 0.2142          | 0.1511 |
| 0.2082        | 12.68 | 9500  | 0.2386          | 0.1467 |
| 0.1814        | 13.35 | 10000 | 0.2301          | 0.1448 |
| 0.1836        | 14.02 | 10500 | 0.2302          | 0.1446 |
| 0.18          | 14.69 | 11000 | 0.2244          | 0.1445 |
| 0.1756        | 15.35 | 11500 | 0.2280          | 0.1439 |
| 0.1693        | 16.02 | 12000 | 0.2307          | 0.1426 |
| 0.1588        | 16.69 | 12500 | 0.2164          | 0.1422 |
| 0.1587        | 17.36 | 13000 | 0.2198          | 0.1417 |
| 0.1738        | 18.02 | 13500 | 0.2282          | 0.1411 |
| 0.1524        | 18.69 | 14000 | 0.2274          | 0.1394 |
| 0.1569        | 19.36 | 14500 | 0.2178          | 0.1396 |
| 0.1433        | 20.03 | 15000 | 0.2200          | 0.1413 |
| 0.1512        | 20.69 | 15500 | 0.2193          | 0.1382 |
| 0.1375        | 21.36 | 16000 | 0.2174          | 0.1393 |
| 0.1302        | 22.03 | 16500 | 0.2246          | 0.1391 |
| 0.146         | 22.7  | 17000 | 0.2222          | 0.1392 |
| 0.1265        | 23.36 | 17500 | 0.2188          | 0.1391 |


### Framework versions

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