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Text To Speech Synthesis On Ljspeech

Metrics

Audio Quality MOS

Results

Performance results of various models on this benchmark

Model Name
Audio Quality MOS
Paper TitleRepository
FastSpeech (Mel + WaveGlow)3.84FastSpeech: Fast, Robust and Controllable Text to Speech-
FastDiff-TTS4.03FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis-
FastSpeech 2 + HiFiGAN4.34NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality-
Grad-TTS + HiFiGAN (1000 steps)4.37Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech-
Flowtron-Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis-
Transformer TTS (Mel + WaveGlow)3.88Neural Speech Synthesis with Transformer Network-
VITS4.43NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality-
Matcha-TTS-Matcha-TTS: A fast TTS architecture with conditional flow matching-
Tacotron 2-Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis-
FastSpeech 2 + HiFiGAN4.32FastSpeech 2: Fast and High-Quality End-to-End Text to Speech-
temp1.25--
FastDiff (4 steps)4.28FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis-
OverFlow3.37OverFlow: Putting flows on top of neural transducers for better TTS-
NaturalSpeech4.56NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality-
Merlin2.4FastSpeech: Fast, Robust and Controllable Text to Speech-
Glow-TTS + HiFiGAN4.34Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search-
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Text To Speech Synthesis On Ljspeech | SOTA | HyperAI