---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-object
  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-object

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the MatsRooth/object dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1285
- Accuracy: 0.9836

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- 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_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6367        | 1.0   | 84   | 1.2848          | 0.9335   |
| 0.7071        | 2.0   | 168  | 0.5108          | 0.9683   |
| 0.4246        | 3.0   | 252  | 0.3018          | 0.9747   |
| 0.3173        | 4.0   | 336  | 0.2098          | 0.9768   |
| 0.2585        | 5.0   | 420  | 0.1685          | 0.9784   |
| 0.192         | 6.0   | 504  | 0.1285          | 0.9836   |
| 0.1772        | 7.0   | 588  | 0.1210          | 0.9810   |
| 0.1435        | 8.0   | 672  | 0.1141          | 0.9805   |
| 0.1425        | 9.0   | 756  | 0.1094          | 0.9836   |
| 0.1398        | 10.0  | 840  | 0.1077          | 0.9826   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.0