Disclaimer: This is a third-party implementation. The original authors stated that they will be releasing code soon.

A recent research showed that fully-convolutional GAN called MelGAN can invert mel-spectrogram into raw audio in non-autoregressive manner. They showed that their MelGAN is lighter & faster than WaveGlow, and even can generalize to unseen speakers when trained on 3 male + 3 female speakers’ speech.

I thought this is a major breakthrough in TTS reserach, since both researchers and engineers can benefit from this fast & lightweight neural vocoder. So I’ve tried to implement this is PyTorch: see GitHub link w/ audio samples below.

Debugging was quite painful while implementing this. Changing the update order of G/D mattered much, and my generator’s loss curve is still going up. (Though results looks good when compared to original paper’s.)

• original paper: https://arxiv.org/abs/1910.06711
• implementation: https://github.com/seungwonpark/melgan
• audio samples: http://swpark.me/melgan/
• audio samples from original paper: https://melgan-neurips.github.io

Figure 1 from “MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis”