---
license: cc-by-nc-4.0
base_model: nguyenvulebinh/wav2vec2-base-vi
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2_base_vietnamese_control_dataset
  results: []
---

<!-- 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_vietnamese_control_dataset

This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vi](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0491
- Wer: 0.2007

## 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: 1e-05
- train_batch_size: 16
- 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: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 19.8101       | 2.05  | 500   | 17.9020         | 1.0    |
| 12.7254       | 4.1   | 1000  | 13.4585         | 1.0    |
| 8.6927        | 6.15  | 1500  | 8.4217          | 1.0    |
| 5.6269        | 8.2   | 2000  | 5.1617          | 1.0    |
| 3.9616        | 10.25 | 2500  | 3.6167          | 1.0    |
| 3.2881        | 12.3  | 3000  | 3.2056          | 1.0    |
| 3.1381        | 14.34 | 3500  | 3.1174          | 1.0    |
| 3.092         | 16.39 | 4000  | 3.1044          | 1.0    |
| 3.0222        | 18.44 | 4500  | 2.9461          | 1.0    |
| 2.8394        | 20.49 | 5000  | 2.7133          | 1.0    |
| 2.6096        | 22.54 | 5500  | 2.3871          | 1.0    |
| 2.3244        | 24.59 | 6000  | 2.0311          | 1.0    |
| 1.9785        | 26.64 | 6500  | 1.6117          | 1.0    |
| 1.6346        | 28.69 | 7000  | 1.2173          | 1.0    |
| 1.3122        | 30.74 | 7500  | 0.9547          | 1.0    |
| 1.0927        | 32.79 | 8000  | 0.7738          | 1.0    |
| 0.9261        | 34.84 | 8500  | 0.6172          | 0.8970 |
| 0.7336        | 36.89 | 9000  | 0.4357          | 0.3654 |
| 0.5754        | 38.93 | 9500  | 0.3304          | 0.3071 |
| 0.4791        | 40.98 | 10000 | 0.2668          | 0.2785 |
| 0.4212        | 43.03 | 10500 | 0.2240          | 0.2548 |
| 0.3439        | 45.08 | 11000 | 0.1852          | 0.2329 |
| 0.3048        | 47.13 | 11500 | 0.1607          | 0.2119 |
| 0.2684        | 49.18 | 12000 | 0.1376          | 0.2105 |
| 0.2298        | 51.23 | 12500 | 0.1227          | 0.2071 |
| 0.2192        | 53.28 | 13000 | 0.1092          | 0.2055 |
| 0.2063        | 55.33 | 13500 | 0.0990          | 0.2039 |
| 0.1875        | 57.38 | 14000 | 0.0895          | 0.2039 |
| 0.1692        | 59.43 | 14500 | 0.0822          | 0.2039 |
| 0.1421        | 61.48 | 15000 | 0.0766          | 0.2029 |
| 0.1505        | 63.52 | 15500 | 0.0710          | 0.2031 |
| 0.1796        | 65.57 | 16000 | 0.0682          | 0.2019 |
| 0.1265        | 67.62 | 16500 | 0.0641          | 0.2015 |
| 0.1172        | 69.67 | 17000 | 0.0617          | 0.2019 |
| 0.1173        | 71.72 | 17500 | 0.0586          | 0.2011 |
| 0.1226        | 73.77 | 18000 | 0.0568          | 0.2015 |
| 0.1165        | 75.82 | 18500 | 0.0567          | 0.2011 |
| 0.1098        | 77.87 | 19000 | 0.0547          | 0.2007 |
| 0.0996        | 79.92 | 19500 | 0.0537          | 0.2009 |
| 0.1024        | 81.97 | 20000 | 0.0521          | 0.2009 |
| 0.0992        | 84.02 | 20500 | 0.0508          | 0.2009 |
| 0.1008        | 86.07 | 21000 | 0.0510          | 0.2009 |
| 0.1147        | 88.11 | 21500 | 0.0501          | 0.2007 |
| 0.1138        | 90.16 | 22000 | 0.0500          | 0.2005 |
| 0.0939        | 92.21 | 22500 | 0.0493          | 0.2007 |
| 0.1021        | 94.26 | 23000 | 0.0492          | 0.2005 |
| 0.1009        | 96.31 | 23500 | 0.0488          | 0.2009 |
| 0.0935        | 98.36 | 24000 | 0.0491          | 0.2007 |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3