PromptTTS++: Controlling Speaker Identity in Prompt-Based Text-to-Speech Using Natural Language Descriptions

no code implementations • 15 Sep 2023 • Reo Shimizu, Ryuichi Yamamoto, Masaya Kawamura, Yuma Shirahata, Hironori Doi, Tatsuya Komatsu, Kentaro Tachibana

We propose PromptTTS++, a prompt-based text-to-speech (TTS) synthesis system that allows control over speaker identity using natural language descriptions.

Lightweight and High-Fidelity End-to-End Text-to-Speech with Multi-Band Generation and Inverse Short-Time Fourier Transform

1 code implementation • 28 Oct 2022 • Masaya Kawamura, Yuma Shirahata, Ryuichi Yamamoto, Kentaro Tachibana

We propose a lightweight end-to-end text-to-speech model using multi-band generation and inverse short-time Fourier transform.

Knowledge Distillation

Period VITS: Variational Inference with Explicit Pitch Modeling for End-to-end Emotional Speech Synthesis

no code implementations • 28 Oct 2022 • Yuma Shirahata, Ryuichi Yamamoto, Eunwoo Song, Ryo Terashima, Jae-Min Kim, Kentaro Tachibana

From these features, the proposed periodicity generator produces a sample-level sinusoidal source that enables the waveform decoder to accurately reproduce the pitch.

Emotional Speech Synthesis Variational Inference

Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation

no code implementations • 21 Apr 2022 • Ryo Terashima, Ryuichi Yamamoto, Eunwoo Song, Yuma Shirahata, Hyun-Wook Yoon, Jae-Min Kim, Kentaro Tachibana

Because pitch-shift data augmentation enables the coverage of a variety of pitch dynamics, it greatly stabilizes training for both VC and TTS models, even when only 1, 000 utterances of the target speaker's neutral data are available.

Data Augmentation Voice Conversion