magpie_tts_multilingual_357m

MagpieTTS Multilingual 357M img#model-badge { display: inline; } | | -lightgrey#model-badge) 🤗 **Hugging Face MagpieTTS Multilingual demo**: magpie_tts_multilingual_demo 💻 **NeMo Speech Framework**: github.com/NVIDIA-NeMo/Speech [!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. Model Details **Developed by:** NVIDIA **Model type:** Multilingual text-to-speech (encoder–decoder transformer, 364M parameters) **Languages:** ar, de, en, es, fr, hi, it, ja, ko, pt, vi, zh **License:** NVIDIA Open Model License **Version:** v2607 (latest); previous: v2602, v2512 **Library:** NeMo Speech Model Description 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 ze