---
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: 0.2294
- Wer: 0.1457

## 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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 13.1354       | 0.67  | 500   | 3.0881          | 1.0186 |
| 2.2088        | 1.34  | 1000  | 0.9805          | 0.4257 |
| 1.122         | 2.0   | 1500  | 0.4928          | 0.2850 |
| 0.7567        | 2.67  | 2000  | 0.4217          | 0.2466 |
| 0.627         | 3.34  | 2500  | 0.3889          | 0.2212 |
| 0.5369        | 4.01  | 3000  | 0.3496          | 0.2131 |
| 0.4485        | 4.67  | 3500  | 0.3239          | 0.1994 |
| 0.4478        | 5.34  | 4000  | 0.3143          | 0.1944 |
| 0.4013        | 6.01  | 4500  | 0.2989          | 0.1871 |
| 0.4542        | 6.68  | 5000  | 0.2996          | 0.1871 |
| 0.351         | 7.34  | 5500  | 0.2719          | 0.1736 |
| 0.3236        | 8.01  | 6000  | 0.2865          | 0.1702 |
| 0.2954        | 8.68  | 6500  | 0.2708          | 0.1636 |
| 0.3533        | 9.35  | 7000  | 0.2712          | 0.1639 |
| 0.2996        | 10.01 | 7500  | 0.2609          | 0.1621 |
| 0.2595        | 10.68 | 8000  | 0.2450          | 0.1627 |
| 0.2914        | 11.35 | 8500  | 0.2748          | 0.1596 |
| 0.253         | 12.02 | 9000  | 0.2496          | 0.1552 |
| 0.2314        | 12.68 | 9500  | 0.2496          | 0.1549 |
| 0.2232        | 13.35 | 10000 | 0.2594          | 0.1557 |
| 0.2206        | 14.02 | 10500 | 0.2485          | 0.1529 |
| 0.2026        | 14.69 | 11000 | 0.2365          | 0.1522 |
| 0.2009        | 15.35 | 11500 | 0.2396          | 0.1513 |
| 0.205         | 16.02 | 12000 | 0.2433          | 0.1499 |
| 0.207         | 16.69 | 12500 | 0.2363          | 0.1496 |
| 0.1895        | 17.36 | 13000 | 0.2280          | 0.1481 |
| 0.1991        | 18.02 | 13500 | 0.2352          | 0.1481 |
| 0.2109        | 18.69 | 14000 | 0.2353          | 0.1477 |
| 0.1959        | 19.36 | 14500 | 0.2294          | 0.1457 |


### Framework versions

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