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MiniMax-Speech: Intrinsic Zero-Shot Text-to-Speech with a Learnable Speaker Encoder
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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.
Forward citations
Cited by 6 Pith papers
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RobustSpeechFlow: Learning Robust Text-to-Speech Trajectories via Augmentation-based Contrastive Flow Matching
RobustSpeechFlow improves TTS alignment robustness by extending contrastive flow matching with length-preserving repeat and skip latent augmentations, lowering WER from 1.44 to 1.38 on Seed-TTS-eval and CER on ZERO500.
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TTS-1 Technical Report
A new TTS system family combines pre-training, supervised fine-tuning, and GRPO reinforcement learning with a 48 kHz codec to produce multilingual speech with in-context voice cloning.
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A new attack framework, TrojanClimb, shows that adversaries can place models with embedded backdoors or biases on public leaderboards while retaining competitive rankings, across text embeddings, text generation, spee...
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A 9B SpeechLM trained on 1.38M hours of English/Korean data plus a full-duplex chat extension trained on 119K hours of time-aligned dialogue outperform same-size audio models on speech tasks and FDB turn-taking metrics.
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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts
MoE-TTS adds frozen text-expert MoE modules to a Qwen3-based TTS system and reports better out-of-domain description alignment than ElevenLabs and MiniMax on a small hand-built test set.
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Quantize More, Lose Less: Autoregressive Generation from Residually Quantized Speech Representations
QTTS models speech as sequences from a multi-codebook RVQ audio codec whose first codebook is trained with ASR supervision, aiming for higher-fidelity TTS than single-codebook systems.
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