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

• URL: http://arxiv.org/abs/2203.16852v1
• Date: Thu, 31 Mar 2022 07:25:11 GMT
• Title: JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech
• Authors: Dan Lim, Sunghee Jung, Eesung Kim
• Abstract summary: We present end-to-end text-to-speech (E2E-TTS) model which has a simplified training pipeline and outperforms a cascade of separately learned models. Our proposed model is jointly trained FastSpeech2 and HiFi-GAN with an alignment module. Experiments on LJSpeech corpus shows that the proposed model outperforms publicly available, state-of-the-art implementations of ESPNet2-TTS.
• Score: 7.476901945542385
• License: http://creativecommons.org/licenses/by/4.0/
• 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 end-to-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, state-of-the-art implementations of ESPNet2-TTS on subjective evaluation (MOS) and some objective evaluations.

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