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https://github.com/ksasi/add
Audio Deepfake Detection
https://github.com/ksasi/add
Last synced: 17 days ago
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Audio Deepfake Detection
- Host: GitHub
- URL: https://github.com/ksasi/add
- Owner: ksasi
- Created: 2024-04-25T18:50:17.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-04-28T07:50:12.000Z (8 months ago)
- Last Synced: 2024-10-16T12:48:42.592Z (2 months ago)
- Language: Python
- Size: 686 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Audio Deepfake Detection - add
### Code:
Below are the step to setup the code and perform training
### Setup:
After setting up the code as below, update the paths appropriately
> git clone https://github.com/ksasi/add.git
>
> git clone https://github.com/TakHemlata/SSL_Anti-spoofing.git
### Install Dependencies:
> cd add
>
> pip install -r requirements.txt
>
> cd SSL_Anti-spoofing
>
> pip install -r requirements.txt
- Copy all the files in `code` folder of `add` repository into `SSL_Anti-spoofing` folder
## Audio Deepfake Detection
### Datasets :
- Create and change directory to ***datasets*** under ***add***
- Download [Custom Dataset] (https://iitjacin-my.sharepoint.com/:u:/g/personal/ranjan_4_iitj_ac_in/EY95OumOlZ5NpIK6qWTKwmwBKiRmhKzDkQ5jpjt1NKGTPw?e=rtbmh8)
- Download [FOR dataset] (https://www.eecs.yorku.ca/~bil/Datasets/for-2sec.tar.gz)
The datasets have the following structure after extraction :
```
Custom Dataset/FOR dataset
data
├── Real
│ │
│ │ Central Avenue 3.wav
│ │ RealGlindaOriginalVoice.mp3
│ │ ...
├── Fake
│ │
│ │ Anthony+US.wav
│ │ 3ae86dc151041de1e2bdbf8de03f42b3.mp3
│ │ ...
```
### Models :
- Create and change directory to ***models*** under ***add***
- Download Pre-trained SSL antispoofing models for LA and DF from [here](https://drive.google.com/drive/folders/1c4ywztEVlYVijfwbGLl9OEa1SNtFKppB?usp=sharing)
### Models Evaluation of SSL W2V model trained for LA and DF tracks of the ASVSpoof dataset (With Custom Dataset):
Execute the below scripts to evaluate SSL W2V model trained for LA and DF tracks of the ASVSpoof dataset with Custom Dataset
- LA Track
> cd SSL_Anti-spoofing
>
> nohup python \/SSL\_Anti-spoofing/evaluate\_model\_dataset.py --track='LA' --dataset\_path='\/add/datasets/Dataset\_Speech\_Assignment' --model_path='\/add/models/LA\_model.pth' --batch\_size=128 >> \/add/logs/custom\_dataset\_LA\_eval.log &
>
>
- DF Track
> cd SSL_Anti-spoofing
>
> nohup python \/SSL\_Anti-spoofing/evaluate\_model\_dataset.py --track='DF' --dataset\_path='\/add/datasets/Dataset\_Speech\_Assignment' --model_path='\/add/models/Best\_LA\_model\_for\_DF.pth' --batch\_size=128 >> \/add/logs/custom\_dataset\_DF\_eval.log &
>
>
### Models Finetuning (With FOR datataset) :
Execute the below scripts to finetune both LA and DF track models on FOR dataset
- LA Track
> cd SSL_Anti-spoofing
>
>
>
> nohup python \/SSL\_Anti-spoofing/finetune\_model\_dataset.py --track='LA' --dataset\_path='\/add/datasets/for-2seconds' --model\_path='\/add/models/LA\_model.pth' --batch_size=128 --dataset\_name='FOR' --epochs=30 --learning\_rate=0.0001 --weight\_decay=1e-4 --save\_path='\/add/models' --comment='finetune' >> \/add/logs/for\_dataset\_LA\_finetune.log &
>
>
- DF Track
> cd SSL_Anti-spoofing
>
>
> nohup python \/SSL\_Anti-spoofing/finetune\_model\_dataset.py --track='DF' --dataset\_path='\/add/datasets/for-2seconds' --model\_path='\/add/models/Best\_LA\_model\_for\_DF.pth' --batch\_size=128 --dataset\_name='FOR' --epochs=30 --learning\_rate=0.0001 --weight\_decay=1e-4 --save\_path='\/add/models' --comment='finetune' >> \/add/logs/for\_dataset\_DF\_finetune.log &
>
>
### Models Evaluation of finetuned models for LA and DF tracks (With FOR test dataset):
Execute the below scripts to evaluate finetuned models of both LA and DF tracks on FOR test dataset, after selecting the best checkpoints
- LA Track
> cd SSL_Anti-spoofing
>
> nohup python \/SSL\_Anti-spoofing/evaluate\_model\_dataset.py --track='LA' --dataset\_path='\/add/datasets/for-2seconds/testing' --model\_path='\/add/models/model\_LA\_weighted\_CCE\_30\_128\_0.0001\_finetune/epoch\_28.pth' --batch\_size=128 --dataset\_name='FORTest datatset on fine-tuned model' >> \/add/logs/FORTest\_dataset\_LA\_ft\_eval.log &
>
- DF Track
> cd SSL_Anti-spoofing
>
> nohup python \/SSL\_Anti-spoofing/evaluate\_model\_dataset.py --track='DF' --dataset\_path='\/add/datasets/for-2seconds/testing' --model\_path='\/add/models/model\_DF\_weighted\_CCE\_30\_128\_0.0001\_finetune/epoch\_29.pth' --batch\_size=128 --dataset\_name='FORTest datatset on fine-tuned model' >> \/add/logs/FORTest\_dataset\_DF\_ft\_eval.log &
>
>
### Models Evaluation of finetuned models for LA and DF tracks (With custome dataset):
Execute the below scripts to evaluate finetuned models of both LA and DF tracks on custom dataset, after selecting the best checkpoints
- LA Track
> cd SSL_Anti-spoofing
>
> nohup python \/SSL\_Anti-spoofing/evaluate\_model\_dataset.py --track='LA' --dataset\_path='\/add/datasets/Dataset\_Speech\_Assignment' --model\_path='\/add/models/model\_LA\_weighted\_CCE\_30\_128\_0.0001\_finetune/epoch\_28.pth' --batch\_size=128 --dataset\_name='custom datatset on fine-tuned model' >> \/add/logs/custom\_dataset\_LA\_ft\_eval.log &
>
- DF Track
> cd SSL_Anti-spoofing
>
> nohup python \/SSL\_Anti-spoofing/evaluate\_model\_dataset.py --track='DF' --dataset\_path='\/add/datasets/Dataset\_Speech\_Assignment' --model\_path='\/add/models/model\_DF\_weighted\_CCE\_30\_128\_0.0001\_finetune/epoch\_29.pth' --batch\_size=128 --dataset\_name='custom datatset on fine-tuned model' >> \/add/logs/custom\_dataset\_DF\_ft\_eval.log &
>
### Demo (Audio Deepfake Detection) :
Demo of **Audio Deepfake Detection** from audio input can be executed by running `Audio_Deepfake_Detection_Demo.ipynb` ipython notebook in the Demo folder
- Step1
- Step2
### References
- EER Metric - [blog](https://yangcha.github.io/EER-ROC/)
- Torchmetrics - [Link](https://lightning.ai/docs/torchmetrics/stable/audio/scale_invariant_signal_noise_ratio.html)
- SSL_Anti-spoofing - [Link](https://github.com/TakHemlata/SSL_Anti-spoofing)
- Gradio - [Link](https://www.gradio.app/)
- Weights & Biases - [Link](https://wandb.ai/site)