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

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