---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: AST-ASVspoof5-Synthetic-Voice-Detection
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8333451578573963
    - name: F1
      type: f1
      value: 0.8891604695934469
    - name: Precision
      type: precision
      value: 0.9208988192978341
    - name: Recall
      type: recall
      value: 0.8595369289154868
---

<!-- 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. -->

# AST-ASVspoof5-Synthetic-Voice-Detection

This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2821
- Accuracy: 0.8333
- F1: 0.8892
- Precision: 0.9209
- Recall: 0.8595

## 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: 5e-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
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0042        | 1.0   | 22795 | 1.6954          | 0.8470   | 0.8942 | 0.9672    | 0.8314 |
| 0.0           | 2.0   | 45590 | 1.5632          | 0.8489   | 0.9014 | 0.9157    | 0.8875 |
| 0.0           | 3.0   | 68385 | 2.2821          | 0.8333   | 0.8892 | 0.9209    | 0.8595 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1