Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
We introduce Seed-TTS, a family of large-scale autoregressive text-to-speech
(TTS) models capable of generating speech that is virtually indistinguishable
from human speech. Seed-TTS serves as a foundation model for speech generation
and excels in speech in-context learning, achieving performance in speaker
similarity and naturalness that matches ground truth human speech in both
objective and subjective evaluations. With fine-tuning, we achieve even higher
subjective scores across these metrics. Seed-TTS offers superior
controllability over various speech attributes such as emotion and is capable
of generating highly expressive and diverse speech for speakers in the wild.
Furthermore, we propose a self-distillation method for speech factorization, as
well as a reinforcement learning approach to enhance model robustness, speaker
similarity, and controllability. We additionally present a non-autoregressive
(NAR) variant of the Seed-TTS model, named Seed-TTS_DiT, which
utilizes a fully diffusion-based architecture. Unlike previous NAR-based TTS
systems, Seed-TTS_DiT does not depend on pre-estimated phoneme
durations and performs speech generation through end-to-end processing. We
demonstrate that this variant achieves comparable performance to the language
model-based variant and showcase its effectiveness in speech editing. We
encourage readers to listen to demos at
https://bytedancespeech.github.io/seedtts_tech_report.
665fe7555a8aa91644bf73ce
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Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
We introduce Seed-TTS, a family of large-scale autoregressive text-to-speech
(TTS) models capable of generating speech that is virtually indistinguishable
from human speech. Seed-TTS serves as a foundation model for speech generation
and excels in speech in-context learning, achieving performance in speaker
similarity and naturalness that matches ground truth human speech in both
objective and subjective evaluations. With fine-tuning, we achieve even higher
subjective scores across these metrics. Seed-TTS offers superior
controllability over various speech attributes such as emotion and is capable
of generating highly expressive and diverse speech for speakers in the wild.
Furthermore, we propose a self-distillation method for speech factorization, as
well as a reinforcement learning approach to enhance model robustness, speaker
similarity, and controllability. We additionally present a non-autoregressive
(NAR) variant of the Seed-TTS model, named Seed-TTS_DiT, which
utilizes a fully diffusion-based architecture. Unlike previous NAR-based TTS
systems, Seed-TTS_DiT does not depend on pre-estimated phoneme
durations and performs speech generation through end-to-end processing. We
demonstrate that this variant achieves comparable performance to the language
model-based variant and showcase its effectiveness in speech editing. We
encourage readers to listen to demos at
https://bytedancespeech.github.io/seedtts_tech_report.
Seed-TTS is a family of large-scale TTS models that generate high-quality speech with in-context learning, superior controllability, and a non-autoregressive variant using diffusion-based architecture that does not rely on pre-estimated phoneme durations.
autoregressive text-to-speech speech generation speaker similarity naturalness emotion control self-distillation reinforcement learning non-autoregressive diffusion-based architecture phoneme durations speech editing