---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-960h-lv60-self
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: teste_finetunning
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.64
---

<!-- 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. -->

# teste_finetunning

This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2220
- Wer: 0.64

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4245        | 1000.0 | 1000 | 0.4122          | 0.8133 |
| 0.2904        | 2000.0 | 2000 | 0.2220          | 0.64   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1