---
license: cc-by-nc-4.0
base_model: nguyenvulebinh/wav2vec2-base-vi
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-vietnamese-VIVOS-CommonVoice-FOSD-Control-dataset-25e-epochs
  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-VIVOS-CommonVoice-FOSD-Control-dataset-25e-epochs

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: 0.3338
- Wer: 0.1833

## 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: 32
- 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: 25

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 16.1039       | 0.39  | 500   | 21.1164         | 1.0    |
| 10.5383       | 0.77  | 1000  | 15.8037         | 1.0    |
| 7.5435        | 1.16  | 1500  | 9.8785          | 1.0    |
| 5.1426        | 1.55  | 2000  | 5.9691          | 1.0    |
| 3.9112        | 1.93  | 2500  | 4.1400          | 1.0    |
| 3.5159        | 2.32  | 3000  | 3.6877          | 1.0    |
| 3.4056        | 2.71  | 3500  | 3.5166          | 1.0    |
| 3.384         | 3.09  | 4000  | 3.6170          | 1.0    |
| 3.3715        | 3.48  | 4500  | 3.5045          | 1.0    |
| 3.373         | 3.87  | 5000  | 3.4859          | 1.0    |
| 3.3539        | 4.25  | 5500  | 3.4843          | 1.0    |
| 3.3063        | 4.64  | 6000  | 3.3596          | 1.0    |
| 3.0749        | 5.03  | 6500  | 2.8515          | 0.9994 |
| 2.6888        | 5.41  | 7000  | 2.4817          | 1.0000 |
| 2.3404        | 5.8   | 7500  | 2.0490          | 0.9815 |
| 2.0588        | 6.19  | 8000  | 1.7986          | 0.9288 |
| 1.8428        | 6.57  | 8500  | 1.4945          | 0.8332 |
| 1.686         | 6.96  | 9000  | 1.3796          | 0.7640 |
| 1.5399        | 7.35  | 9500  | 1.2362          | 0.6927 |
| 1.4374        | 7.73  | 10000 | 1.1130          | 0.6320 |
| 1.3281        | 8.12  | 10500 | 1.0058          | 0.5705 |
| 1.2308        | 8.51  | 11000 | 0.8888          | 0.5109 |
| 1.1405        | 8.89  | 11500 | 0.8438          | 0.4524 |
| 1.0647        | 9.28  | 12000 | 0.7767          | 0.4208 |
| 1.0104        | 9.67  | 12500 | 0.7385          | 0.3777 |
| 0.9629        | 10.05 | 13000 | 0.6731          | 0.3505 |
| 0.9045        | 10.44 | 13500 | 0.6295          | 0.3317 |
| 0.8573        | 10.83 | 14000 | 0.6071          | 0.3115 |
| 0.8443        | 11.21 | 14500 | 0.5895          | 0.2984 |
| 0.7915        | 11.6  | 15000 | 0.5828          | 0.2823 |
| 0.7965        | 11.99 | 15500 | 0.5552          | 0.2714 |
| 0.7738        | 12.37 | 16000 | 0.5100          | 0.2605 |
| 0.7326        | 12.76 | 16500 | 0.4884          | 0.2499 |
| 0.7007        | 13.15 | 17000 | 0.4799          | 0.2402 |
| 0.6997        | 13.53 | 17500 | 0.4647          | 0.2331 |
| 0.68          | 13.92 | 18000 | 0.4469          | 0.2271 |
| 0.6707        | 14.31 | 18500 | 0.4261          | 0.2231 |
| 0.6557        | 14.69 | 19000 | 0.4145          | 0.2164 |
| 0.6509        | 15.08 | 19500 | 0.4010          | 0.2120 |
| 0.6649        | 15.47 | 20000 | 0.4038          | 0.2092 |
| 0.6191        | 15.85 | 20500 | 0.3926          | 0.2064 |
| 0.6385        | 16.24 | 21000 | 0.3882          | 0.2024 |
| 0.6222        | 16.63 | 21500 | 0.3874          | 0.2016 |
| 0.5792        | 17.01 | 22000 | 0.3873          | 0.2023 |
| 0.5775        | 17.4  | 22500 | 0.3757          | 0.1975 |
| 0.5647        | 17.79 | 23000 | 0.3626          | 0.1964 |
| 0.5723        | 18.17 | 23500 | 0.3574          | 0.1958 |
| 0.5573        | 18.56 | 24000 | 0.3530          | 0.1960 |
| 0.5813        | 18.95 | 24500 | 0.3541          | 0.1933 |
| 0.563         | 19.33 | 25000 | 0.3455          | 0.1926 |
| 0.5402        | 19.72 | 25500 | 0.3483          | 0.1910 |
| 0.5578        | 20.11 | 26000 | 0.3516          | 0.1915 |
| 0.5456        | 20.49 | 26500 | 0.3477          | 0.1878 |
| 0.5453        | 20.88 | 27000 | 0.3391          | 0.1882 |
| 0.5265        | 21.27 | 27500 | 0.3386          | 0.1869 |
| 0.557         | 21.66 | 28000 | 0.3388          | 0.1864 |
| 0.5526        | 22.04 | 28500 | 0.3373          | 0.1864 |
| 0.5284        | 22.43 | 29000 | 0.3352          | 0.1854 |
| 0.5351        | 22.82 | 29500 | 0.3373          | 0.1850 |
| 0.5775        | 23.2  | 30000 | 0.3382          | 0.1848 |
| 0.5292        | 23.59 | 30500 | 0.3371          | 0.1843 |
| 0.52          | 23.98 | 31000 | 0.3338          | 0.1839 |
| 0.5372        | 24.36 | 31500 | 0.3337          | 0.1829 |
| 0.5167        | 24.75 | 32000 | 0.3338          | 0.1833 |


### Framework versions

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