---
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-ASVspoof2019-Synthetic-Voice-Detection-New
  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: 0.9970616647882788
    - name: F1
      type: f1
      value: 0.9983654642753185
    - name: Precision
      type: precision
      value: 0.9968253968253968
    - name: Recall
      type: recall
      value: 0.9999102978112666
---

<!-- 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-ASVspoof2019-Synthetic-Voice-Detection-New

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: 0.0213
- Accuracy: 0.9971
- F1: 0.9984
- Precision: 0.9968
- Recall: 0.9999

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0232        | 1.0   | 3173 | 0.0404          | 0.9932   | 0.9962 | 0.9934    | 0.9991 |
| 0.0058        | 2.0   | 6346 | 0.0383          | 0.9931   | 0.9962 | 0.9927    | 0.9996 |
| 0.0014        | 3.0   | 9519 | 0.0213          | 0.9971   | 0.9984 | 0.9968    | 0.9999 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1