title: MiniMax-Speech: Intrinsic Zero-Shot Text-to-Speech with a Learnable
    Speaker Encoder
summary: 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.
paper_authors._id: 68244ea3bfb1b25f60400efd
paper_authors.name: Bowen Zhang
paper_authors._id: 68244ea3bfb1b25f60400efe
paper_authors.name: Congchao Guo
paper_authors._id: 68244ea3bfb1b25f60400eff
paper_authors.name: Geng Yang
paper_authors._id: 68244ea3bfb1b25f60400f00
paper_authors.name: Hang Yu
paper_authors._id: 68244ea3bfb1b25f60400f01
paper_authors.name: Haozhe Zhang
paper_authors._id: 68244ea3bfb1b25f60400f02
paper_authors.name: Heidi Lei
paper_authors._id: 68244ea3bfb1b25f60400f03
paper_authors.name: Jialong Mai
paper_authors._id: 68244ea3bfb1b25f60400f04
paper_authors.name: Junjie Yan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T10:05:37.903Z
paper_authors._id: 63390ce41718795719635b1e
paper_authors.avatarUrl: /avatars/ad03a2b349f01c1ac1fedfb95d02d43e.svg
paper_authors.fullname: JunjieYan
paper_authors.type: user
paper_authors.user: JunjieYan
paper_authors._id: 68244ea3bfb1b25f60400f05
paper_authors.name: Kaiyue Yang
paper_authors._id: 68244ea3bfb1b25f60400f06
paper_authors.name: Mingqi Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T10:05:51.310Z
paper_authors._id: 65e29a93e142ecfc09bddf3a
paper_authors.avatarUrl: /avatars/70168cae7aef1bb2c00392b926eabb18.svg
paper_authors.fullname: Mingqi Yang
paper_authors.type: user
paper_authors.user: mqyang1s
paper_authors._id: 68244ea3bfb1b25f60400f07
paper_authors.name: Peikai Huang
paper_authors._id: 68244ea3bfb1b25f60400f08
paper_authors.name: Ruiyang Jin
paper_authors._id: 68244ea3bfb1b25f60400f09
paper_authors.name: Sitan Jiang
paper_authors._id: 68244ea3bfb1b25f60400f0a
paper_authors.name: Weihua Cheng
paper_authors._id: 68244ea3bfb1b25f60400f0b
paper_authors.name: Yawei Li
paper_authors._id: 68244ea3bfb1b25f60400f0c
paper_authors.name: Yichen Xiao
paper_authors._id: 68244ea3bfb1b25f60400f0d
paper_authors.name: Yiying Zhou
paper_authors._id: 68244ea3bfb1b25f60400f0e
paper_authors.name: Yongmao Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T10:06:55.523Z
paper_authors._id: 64b655c3f44a33a87e73b866
paper_authors.avatarUrl: /avatars/3a2c58eb10d4cf7040f63ea15284c574.svg
paper_authors.fullname: yongmao zhang
paper_authors.type: user
paper_authors.user: ymzhang0519
paper_authors._id: 68244ea3bfb1b25f60400f0f
paper_authors.name: Yuan Lu
paper_authors._id: 68244ea3bfb1b25f60400f10
paper_authors.name: Yucen He
paper_title: MiniMax-Speech: Intrinsic Zero-Shot Text-to-Speech with a Learnable
    Speaker Encoder
paper_summary: 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.
paper_ai_summary: MiniMax-Speech, an autoregressive Transformer-based TTS model, generates high-quality speech with a learnable speaker encoder that extracts reference speaker features without transcription, achieving SOTA results in voice cloning and supporting various extensions.
paper_ai_keywords: autoregressive Transformer Text-to-Speech TTS learnable speaker encoder timbre features zero-shot one-shot voice cloning Flow-VAE disentangled representations LoRA text to voice T2V professional voice cloning PVC