FastSpeech
A neural network-based text-to-speech model that substantially improves speech naturalness compared with traditional concatenative and parametric synthesis methods.
Timeline (last 90 days)
Traditional TTS produces mechanical, robotic-sounding voices with limited customization space but low resource usage; neural TTS relies on architectures such as Tacotron, WaveNet, and FastSpeech and delivers more natural audio quality but with higher GPU/CPU overhead.
FastSpeech / FastSpeech 2 is a non-autoregressive architecture that generates the entire spectrogram in parallel at once, solving Tacotron's problems of slow autoregressive decoding and skipped or repeated words, and is the default choice for many current open-source and commercial systems.
End-to-end architectures such as Tacotron, FastSpeech, and VITS have compressed the overall text-to-audio latency from several seconds to hundreds of milliseconds.
All Facts (4)
当前主流的神经网络TTS方案包括Tacotron系列、FastSpeech系列以及VITS、Bark等端到端模型
85%UnverifiedTraditional TTS produces mechanical, robotic-sounding voices with limited customization space but low resource usage; neural TTS relies on architectures such as Tacotron, WaveNet, and FastSpeech and delivers more natural audio quality but with higher GPU/CPU overhead.
50%UnverifiedFastSpeech / FastSpeech 2 is a non-autoregressive architecture that generates the entire spectrogram in parallel at once, solving Tacotron's problems of slow autoregressive decoding and skipped or repeated words, and is the default choice for many current open-source and commercial systems.
50%UnverifiedEnd-to-end architectures such as Tacotron, FastSpeech, and VITS have compressed the overall text-to-audio latency from several seconds to hundreds of milliseconds.
50%