---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- vivos
metrics:
- wer
model-index:
- name: Wave2Vec2_OV_Vie
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: vivos
      type: vivos
      config: default
      split: test
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 1.0
---

<!-- 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. -->

# Wave2Vec2_OV_Vie

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the vivos dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5908
- 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| No log        | 0.27  | 100  | 3.9210          | 1.0 |
| No log        | 0.55  | 200  | 3.4375          | 1.0 |
| No log        | 0.82  | 300  | 3.4356          | 1.0 |
| No log        | 1.1   | 400  | 3.4045          | 1.0 |
| 4.1866        | 1.37  | 500  | 3.4694          | 1.0 |
| 4.1866        | 1.65  | 600  | 3.6266          | 1.0 |
| 4.1866        | 1.92  | 700  | 3.5694          | 1.0 |
| 4.1866        | 2.19  | 800  | 3.5733          | 1.0 |
| 4.1866        | 2.47  | 900  | 3.6381          | 1.0 |
| 3.4376        | 2.74  | 1000 | 3.6604          | 1.0 |
| 3.4376        | 3.02  | 1100 | 3.5868          | 1.0 |
| 3.4376        | 3.29  | 1200 | 3.4988          | 1.0 |
| 3.4376        | 3.57  | 1300 | 3.5409          | 1.0 |
| 3.4376        | 3.84  | 1400 | 3.4883          | 1.0 |
| 3.4365        | 4.12  | 1500 | 3.6125          | 1.0 |
| 3.4365        | 4.39  | 1600 | 3.6123          | 1.0 |
| 3.4365        | 4.66  | 1700 | 3.5978          | 1.0 |
| 3.4365        | 4.94  | 1800 | 3.5693          | 1.0 |
| 3.4365        | 5.21  | 1900 | 3.5659          | 1.0 |
| 3.4339        | 5.49  | 2000 | 3.6234          | 1.0 |
| 3.4339        | 5.76  | 2100 | 3.5997          | 1.0 |
| 3.4339        | 6.04  | 2200 | 3.6529          | 1.0 |
| 3.4339        | 6.31  | 2300 | 3.5780          | 1.0 |
| 3.4339        | 6.58  | 2400 | 3.5844          | 1.0 |
| 3.4333        | 6.86  | 2500 | 3.5792          | 1.0 |
| 3.4333        | 7.13  | 2600 | 3.5468          | 1.0 |
| 3.4333        | 7.41  | 2700 | 3.5691          | 1.0 |
| 3.4333        | 7.68  | 2800 | 3.5408          | 1.0 |
| 3.4333        | 7.96  | 2900 | 3.5482          | 1.0 |
| 3.4294        | 8.23  | 3000 | 3.6070          | 1.0 |
| 3.4294        | 8.5   | 3100 | 3.5905          | 1.0 |
| 3.4294        | 8.78  | 3200 | 3.6018          | 1.0 |
| 3.4294        | 9.05  | 3300 | 3.6326          | 1.0 |
| 3.4294        | 9.33  | 3400 | 3.6214          | 1.0 |
| 3.4293        | 9.6   | 3500 | 3.6372          | 1.0 |
| 3.4293        | 9.88  | 3600 | 3.6215          | 1.0 |
| 3.4293        | 10.15 | 3700 | 3.5106          | 1.0 |
| 3.4293        | 10.43 | 3800 | 3.5066          | 1.0 |
| 3.4293        | 10.7  | 3900 | 3.5352          | 1.0 |
| 3.4295        | 10.97 | 4000 | 3.5129          | 1.0 |
| 3.4295        | 11.25 | 4100 | 3.6384          | 1.0 |
| 3.4295        | 11.52 | 4200 | 3.6019          | 1.0 |
| 3.4295        | 11.8  | 4300 | 3.5876          | 1.0 |
| 3.4295        | 12.07 | 4400 | 3.6207          | 1.0 |
| 3.4252        | 12.35 | 4500 | 3.5998          | 1.0 |
| 3.4252        | 12.62 | 4600 | 3.6216          | 1.0 |
| 3.4252        | 12.89 | 4700 | 3.6073          | 1.0 |
| 3.4252        | 13.17 | 4800 | 3.5567          | 1.0 |
| 3.4252        | 13.44 | 4900 | 3.5745          | 1.0 |
| 3.4274        | 13.72 | 5000 | 3.5738          | 1.0 |
| 3.4274        | 13.99 | 5100 | 3.5914          | 1.0 |
| 3.4274        | 14.27 | 5200 | 3.6004          | 1.0 |
| 3.4274        | 14.54 | 5300 | 3.5968          | 1.0 |
| 3.4274        | 14.81 | 5400 | 3.5908          | 1.0 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3