---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- vivos
metrics:
- wer
model-index:
- name: wav2vec2-vivos-asr
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: vivos
      type: vivos
      config: default
      split: None
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.23381058715355313
---

<!-- 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-vivos-asr

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: 0.3492
- Wer: 0.2338

## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 8.4226        | 2.0548  | 150  | 4.9423          | 1.0    |
| 3.59          | 4.1096  | 300  | 3.6898          | 1.0    |
| 3.4271        | 6.1644  | 450  | 3.5183          | 1.0    |
| 2.6948        | 8.2192  | 600  | 1.2770          | 0.8026 |
| 0.7372        | 10.2740 | 750  | 0.5197          | 0.3625 |
| 0.4012        | 12.3288 | 900  | 0.4108          | 0.2911 |
| 0.2974        | 14.3836 | 1050 | 0.3732          | 0.2604 |
| 0.2737        | 16.4384 | 1200 | 0.3550          | 0.2393 |
| 0.2108        | 18.4932 | 1350 | 0.3565          | 0.2434 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1