Total: 1
Three research directions that have recently advanced the text-to-speech (TTS) field are end-to-end architecture, prosody control modeling, and on-the-fly duration alignment of non-auto-regressive models. However, these three agendas have yet to be tackled at once in a single solution. Current studies are limited either by a lack of control over prosody modeling or by the inefficient training inherent in building a two-stage TTS pipeline. We propose TriniTTS, a pitch-controllable end-to-end TTS without an external aligner that generates natural speech by addressing the issues mentioned above at once. It eliminates the training inefficiency in the two-stage TTS pipeline by the end-to-end architecture. Moreover, it manages to learn the latent vector representing the data distribution of the speeches through performing tasks (alignment search, pitch estimation, waveform generation) simultaneously. Experimental results demonstrate that TriniTTS enables prosody modeling with user input parameters to generate deterministic speech, while synthesizing comparable speech to the state-of-the-art VITS. Furthermore, eliminating normalizing flow modules used in VITS increases the inference speed by 28.84% in CPU environment and by 29.16% in GPU environment.