Model overview
> [!Note] > July 21, 2026: MagpieTTS v2607 was released with support for 3 new languages (Arabic, Korean, Portuguese). > > For the older checkpoints, refer to below tags: > > - Released on March 2026, v2602. > - Released on January 2026, v2512. MagpieTTS is an end-to-end multilingual neural text-to-speech model that synthesizes speech using 5 English speaker voices — Aria, Jason, Leo, Sofia, and John Van Stan — across 12 languages: Arabic (ar), Chinese (zh), English (en), French (fr), German (de), Hindi (hi), Italian (it), Japanese (ja), Korean (ko), Portuguese (pt), Spanish (es), and Vietnamese (vi). The model adopts a transformer encoder–decoder architecture that autoregressively predicts discrete audio codec tokens, using multi-codebook prediction (typically 8 codebooks) with frame stacking (factor = 2) and a local transformer for fine-grained refinement of high-fidelity audio. To improve robustness and controllability, training incorporates attention priors for stable text-to-audio alignment, classifier-free guidance (CFG) for stronger conditioning, and Group Relative Policy Optimization (GRPO) for preference-aligned generation. At inference time, MagpieTTS supports batched synthesis of complete utterances as well as long-form generation of extended text via a sliding-window mechanism; the predicted codec tokens are then decoded into speech waveforms by a frozen pretrained audio codec model (NanoCodec). This release also removed zero-shot voice-cloning capability for security reasons, and added IPA grapheme-to-phoneme (G2P) support for custom dictionaries and code-switching, and updated G2P support for English-to-Katakana code-switching. MagpieTTS is for developers, researchers, and product teams building multilingual speech applications that need consistent speaker voices across 12 languages. Typical applications include cascade voice agents, audiobook and content narration, accessibility tools, dubbing and localization pipelines, and interactive media. IPA grapheme-to-phoneme (G2P) support for custom dictionaries and code-switching (including English-to-Katakana) also enables mixed-language content and domain-specific pronunciation.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。