---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
base_model: nguyenvulebinh/wav2vec2-base-vi
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: wav2vec2-common-voice-16_1_vi
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: vi
      split: None
      args: vi
    metrics:
    - type: wer
      value: 0.9998983326555511
      name: Wer
---

<!-- 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-common-voice-16_1_vi

This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vi](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5323
- Wer: 0.9999

## 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 21.7841       | 4.2373  | 500  | 7.6684          | 0.9999 |
| 4.0045        | 8.4746  | 1000 | 3.5474          | 0.9999 |
| 3.4763        | 12.7119 | 1500 | 3.5357          | 0.9999 |
| 3.4721        | 16.9492 | 2000 | 3.5319          | 0.9999 |
| 3.4661        | 21.1864 | 2500 | 3.5321          | 0.9999 |
| 3.464         | 25.4237 | 3000 | 3.5315          | 0.9999 |
| 3.4732        | 29.6610 | 3500 | 3.5323          | 0.9999 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1