Avocodo: Generative Adversarial Network for Artifact-free Vocoder
Unofficial implementation of Avocodo: Generative Adversarial Network for Artifact-free Vocoder.
Disclaimer: It only works on config_v1.json for now and this repo build with experimentation purpose not for Production.
- For best quality speech synthesis please visit deepsync.co
Training:
python train.py --config config_v1.json
Notes:
- Avocodo uses same Generator as HiFi-GAN V1 and V2 but using different discriminators for modelling better lower and higher frequencies.
- PQMF is the crucial for both Discriminators.
- Losses are similar to HiFi-GAN.
- Performance and speed both are some what similar to HiFi-GAN.
- Avocodo far better than HiFi-GAN when it comes to synthesize unseen speaker.
- Avocodo training is around 20 % faster than HiFi-GAN also it took very less training to output excellent quality of audio.
Citations:
@misc{https://doi.org/10.48550/arxiv.2206.13404,
doi = {10.48550/ARXIV.2206.13404},
url = {https://arxiv.org/abs/2206.13404},
author = {Bak, Taejun and Lee, Junmo and Bae, Hanbin and Yang, Jinhyeok and Bae, Jae-Sung and Joo, Young-Sun},
keywords = {Audio and Speech Processing (eess.AS), Artificial Intelligence (cs.AI), Sound (cs.SD), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Avocodo: Generative Adversarial Network for Artifact-free Vocoder},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}