RawNet: Fast End-to-End Neural Vocoder

Samples of Paper RawNet: Fast End-to-End Neural Vocoder, which is submitted to Interspeech 2019.

RawNet, is a truly end-to-end neural vocoder, which use a coder network to learn the higher representation of signal, and an autoregressive voder network to generate speech sample by sample. The coder and voder together act like an auto-encoder network, and could be jointly trained directly on raw waveform without any human-designed features.

More detail is explained in the paper.

Code will be released soon...

Samples

In the RawNet samples directory, you can find the demo samples generated by RawNet.

Author

Yunchao He

[email protected]