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---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: deeepfake-audio-A
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8939393939393939
---
<!-- 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. -->
# deeepfake-audio-A
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5791
- Accuracy: 0.8939
## 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: 8
- 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_ratio: 0.01
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.638 | 1.0 | 33 | 0.6019 | 0.7121 |
| 0.4734 | 2.0 | 66 | 0.4665 | 0.8333 |
| 0.4281 | 3.0 | 99 | 0.3324 | 0.8939 |
| 0.2556 | 4.0 | 132 | 0.4255 | 0.8788 |
| 0.196 | 5.0 | 165 | 0.4007 | 0.8939 |
| 0.1557 | 6.0 | 198 | 0.3592 | 0.9091 |
| 0.0951 | 7.0 | 231 | 0.4533 | 0.9091 |
| 0.0505 | 8.0 | 264 | 0.3741 | 0.9242 |
| 0.0475 | 9.0 | 297 | 0.7494 | 0.8333 |
| 0.0394 | 10.0 | 330 | 0.7242 | 0.8636 |
| 0.0034 | 11.0 | 363 | 0.7240 | 0.8636 |
| 0.041 | 12.0 | 396 | 0.7503 | 0.8485 |
| 0.0028 | 13.0 | 429 | 0.6365 | 0.8939 |
| 0.0189 | 14.0 | 462 | 0.5352 | 0.9091 |
| 0.0041 | 15.0 | 495 | 0.5700 | 0.9091 |
| 0.0023 | 16.0 | 528 | 0.5791 | 0.8939 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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