WaveFake: A data set to facilitate audio DeepFake detection
Description
The main purpose of this data set is to facilitate research into audio DeepFakes.
These generated media files have been increasingly used to commit impersonation attempts or online harassment.
The data set consists of 88,600 generated audio clips (16-bit PCM wav).
All of these samples were generated by four different neural network architectures:
Additionally, we examined a bigger version of MelGAN and investigated a variant of Multi-Band MelGAN that computes its auxiliary loss over the full audio instead of its subbands.
Collection Process
For WaveGlow, we utilize the official implementation (commit 8afb643) in conjunction with the official pre-trained network on PyTorch Hub.
We use a popular implementation available on GitHub (commit 12c677e) for the remaining networks.
The repository also offers pre-trained models.
We used the pre-trained networks to generate samples that are similar to their respective training distributions, LJ Speech and JSUT.
When sampling the data set, we first extract Mel spectrograms from the original audio files, using the pre-processing scripts of the corresponding repositories.
We then feed these Mel spectrograms to the respective models to obtain the data set.
This data set is licensed with a CC-BY-SA 4.0 license.
This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -- EXC-2092 CaSa -- 390781972.
Files
generated_audio.zip
Additional details
References
- Kumar, Kundan, et al. "Melgan: Generative adversarial networks for conditional waveform synthesis." arXiv preprint arXiv:1910.06711 (2019).
- Yamamoto, Ryuichi, Eunwoo Song, and Jae-Min Kim. "Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram." ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020.
- Yang, Geng, et al. "Multi-band MelGAN: Faster waveform generation for high-quality text-to-speech." 2021 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2021.
- Prenger, Ryan, Rafael Valle, and Bryan Catanzaro. "Waveglow: A flow-based generative network for speech synthesis." ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.
- Sonobe, Ryosuke, Shinnosuke Takamichi, and Hiroshi Saruwatari. "JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis." arXiv preprint arXiv:1711.00354 (2017).