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---
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
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