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

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: nan
- Wer: 1.0

## 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: 8e-06
- 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: 500
- num_epochs: 30.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.43  | 100  | inf             | 1.2580 |
| No log        | 0.85  | 200  | inf             | 1.0    |
| No log        | 1.28  | 300  | inf             | 1.0    |
| No log        | 1.71  | 400  | inf             | 1.0    |
| 12.36         | 2.14  | 500  | inf             | 1.0    |
| 12.36         | 2.56  | 600  | inf             | 1.0    |
| 12.36         | 2.99  | 700  | inf             | 1.0    |
| 12.36         | 3.42  | 800  | inf             | 1.0    |
| 12.36         | 3.85  | 900  | inf             | 1.0    |
| 8.4674        | 4.27  | 1000 | inf             | 1.0    |
| 8.4674        | 4.7   | 1100 | inf             | 1.0    |
| 8.4674        | 5.13  | 1200 | inf             | 1.0    |
| 8.4674        | 5.56  | 1300 | inf             | 1.0    |
| 8.4674        | 5.98  | 1400 | inf             | 1.0    |
| 6.9866        | 6.41  | 1500 | inf             | 1.0    |
| 6.9866        | 6.84  | 1600 | inf             | 1.0    |
| 6.9866        | 7.26  | 1700 | inf             | 1.0    |
| 6.9866        | 7.69  | 1800 | inf             | 1.0    |
| 6.9866        | 8.12  | 1900 | inf             | 1.0    |
| 6.8089        | 8.55  | 2000 | inf             | 1.0    |
| 6.8089        | 8.97  | 2100 | inf             | 1.0    |
| 6.8089        | 9.4   | 2200 | inf             | 1.0    |
| 6.8089        | 9.83  | 2300 | inf             | 1.0    |
| 6.8089        | 10.26 | 2400 | inf             | 1.0    |
| 6.7847        | 10.68 | 2500 | inf             | 1.0    |
| 6.7847        | 11.11 | 2600 | inf             | 1.0    |
| 6.7847        | 11.54 | 2700 | inf             | 1.0    |
| 6.7847        | 11.97 | 2800 | inf             | 1.0    |
| 6.7847        | 12.39 | 2900 | inf             | 1.0    |
| 6.7941        | 12.82 | 3000 | inf             | 1.0    |
| 6.7941        | 13.25 | 3100 | inf             | 1.0    |
| 6.7941        | 13.68 | 3200 | inf             | 1.0    |
| 6.7941        | 14.1  | 3300 | inf             | 1.0    |
| 6.7941        | 14.53 | 3400 | inf             | 1.0    |
| 6.7956        | 14.96 | 3500 | inf             | 1.0    |
| 6.7956        | 15.38 | 3600 | inf             | 1.0    |
| 6.7956        | 15.81 | 3700 | inf             | 1.0    |
| 6.7956        | 16.24 | 3800 | inf             | 1.0    |
| 6.7956        | 16.67 | 3900 | inf             | 1.0    |
| 6.8102        | 17.09 | 4000 | inf             | 1.0    |
| 6.8102        | 17.52 | 4100 | inf             | 1.0    |
| 6.8102        | 17.95 | 4200 | inf             | 1.0    |
| 6.8102        | 18.38 | 4300 | inf             | 1.0    |
| 6.8102        | 18.8  | 4400 | inf             | 1.0    |
| 6.7761        | 19.23 | 4500 | inf             | 1.0    |
| 6.7761        | 19.66 | 4600 | inf             | 1.0    |
| 6.7761        | 20.09 | 4700 | inf             | 1.0    |
| 6.7761        | 20.51 | 4800 | inf             | 1.0    |
| 6.7761        | 20.94 | 4900 | inf             | 1.0    |
| 6.8063        | 21.37 | 5000 | inf             | 1.0    |
| 6.8063        | 21.79 | 5100 | inf             | 1.0    |
| 6.8063        | 22.22 | 5200 | inf             | 1.0    |
| 6.8063        | 22.65 | 5300 | inf             | 1.0    |
| 6.8063        | 23.08 | 5400 | inf             | 1.0    |
| 6.7934        | 23.5  | 5500 | inf             | 1.0    |
| 6.7934        | 23.93 | 5600 | inf             | 1.0    |
| 6.7934        | 24.36 | 5700 | inf             | 1.0    |
| 6.7934        | 24.79 | 5800 | inf             | 1.0    |
| 6.7934        | 25.21 | 5900 | inf             | 1.0    |
| 6.7819        | 25.64 | 6000 | inf             | 1.0    |
| 6.7819        | 26.07 | 6100 | inf             | 1.0    |
| 6.7819        | 26.5  | 6200 | inf             | 1.0    |
| 6.7819        | 26.92 | 6300 | inf             | 1.0    |
| 6.7819        | 27.35 | 6400 | inf             | 1.0    |
| 6.8278        | 27.78 | 6500 | inf             | 1.0    |
| 6.8278        | 28.21 | 6600 | inf             | 1.0    |
| 6.8278        | 28.63 | 6700 | inf             | 1.0    |
| 6.8278        | 29.06 | 6800 | nan             | 1.0    |
| 6.8278        | 29.49 | 6900 | nan             | 1.0    |
| 6.7427        | 29.91 | 7000 | nan             | 1.0    |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.0
- Datasets 2.14.4
- Tokenizers 0.13.3