MiniMax-Speech: Intrinsic Zero-Shot Text-to-Speech with a Learnable
  Speaker Encoder
Authors: name: Bowen Zhang, name: Congchao Guo, name: Geng Yang, name: Hang Yu, name: Haozhe Zhang, name: Heidi Lei, name: Jialong Mai, name: Junjie Yan, name: Kaiyue Yang, name: Mingqi Yang, name: Peikai Huang, name: Ruiyang Jin, name: Sitan Jiang, name: Weihua Cheng, name: Yawei Li, name: Yichen Xiao, name: Yiying Zhou, name: Yongmao Zhang, name: Yuan Lu, name: Yucen He

We introduce MiniMax-Speech, an autoregressive Transformer-based
Text-to-Speech (TTS) model that generates high-quality speech. A key innovation
is our learnable speaker encoder, which extracts timbre features from a
reference audio without requiring its transcription. This enables
MiniMax-Speech to produce highly expressive speech with timbre consistent with
the reference in a zero-shot manner, while also supporting one-shot voice
cloning with exceptionally high similarity to the reference voice. In addition,
the overall quality of the synthesized audio is enhanced through the proposed
Flow-VAE. Our model supports 32 languages and demonstrates excellent
performance across multiple objective and subjective evaluations metrics.
Notably, it achieves state-of-the-art (SOTA) results on objective voice cloning
metrics (Word Error Rate and Speaker Similarity) and has secured the top
position on the public TTS Arena leaderboard. Another key strength of
MiniMax-Speech, granted by the robust and disentangled representations from the
speaker encoder, is its extensibility without modifying the base model,
enabling various applications such as: arbitrary voice emotion control via
LoRA; text to voice (T2V) by synthesizing timbre features directly from text
description; and professional voice cloning (PVC) by fine-tuning timbre
features with additional data. We encourage readers to visit
https://minimax-ai.github.io/tts_tech_report for more examples.