metadata

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

AST-ASVspoof5-Synthetic-Voice-Detection

This model is a fine-tuned version of 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