Tacotron 2: Neural Network Architecture for Speech Synthesis Directly from Text

This investigation paper describes Tacotron two, a neural community architecture for speech synthesis instantly from textual content.

Right here, the authors present the framework and the over-all composition of the program, detailing all the essential measures expected for productive realistic implementation. The discussed program involves two parts: recurrent sequence-to-sequence function prediction community and a modified edition of WaveNet made use of to deliver time-domain waveforms from mel-scale spectrograms. This textual content also analyzes the training set up and the course of action of audio high quality evaluation .

The code implementations of the proposed program can be located below.

The program is centered on a recurrent sequence-to-sequence function prediction community that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize time-domain waveforms from those spectrograms. Our model achieves a necessarily mean feeling score (MOS) of 4.53 comparable to a MOS of 4.58 for professionally recorded speech. To validate our design possibilities, we present ablation scientific tests of key parts of our program and examine the affect of sing mel spectrograms as the conditioning enter to WaveNet alternatively of linguistic, duration, and F0 features. We even further present that employing this compact acoustic intermediate illustration lets for a substantial reduction in the dimensions of the WaveNet architecture.

Connection to investigation paper: https://arxiv.org/pdf/1712.05884v2.pdf