JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech

Abstract

In neural text-to-speech (TTS), two-stage system or a cascade of separately learned models have shown synthesis quality close to human speech.For example, FastSpeech2 transforms an input text to a mel-spectrogram and then HiFi-GAN generates a raw waveform from a mel-spectogram where they are called an acoustic feature generator and a neural vocoder respectively.However, their training pipeline is somewhat cumbersome in that it requires a fine-tuning and an accurate speech-text alignment for optimal performance.In this work, we present endto-end text-to-speech (E2E-TTS) model which has a simplified training pipeline and outperforms a cascade of separately learned models.Specifically, our proposed model is jointly trained FastSpeech2 and HiFi-GAN with an alignment module.Since there is no acoustic feature mismatch between training and inference, it does not requires fine-tuning.Furthermore, we remove dependency on an external speech-text alignment tool by adopting an alignment learning objective in our joint training framework.Experiments on LJSpeech corpus shows that the proposed model outperforms publicly available, stateof-the-art implementations of ESPNet2-TTS on subjective evaluation (MOS) and some objective evaluations.