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---
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
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