magpie_tts_multilingual

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🤗 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 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.

This model is ready for commercial use.

Key Features

• Multilingual Support — Synthesizes natural speech across all 12 supported languages with consistent speaker identity.
• Expressive Voices — Multiple voice options with emotional tones and gender variations including 4 proprietary voices and 1 public voice.
• Text Normalization — Built-in text normalization for handling numbers, abbreviations, and special characters for all 12 languages.
• Efficient High-Fidelity Decoding — A local transformer with frame stacking (factor = 2) performs multi-codebook refinement on stacked frames, improving audio quality while reducing sequence length for faster generation.

Model Sources

• Demo: magpie_tts_multilingual_demo
• Repository: NVIDIA NeMo Speech
• Enterprise API: MagpieTTS NIM
• Voice-agent examples: NVIDIA voice-agent-examples
• Nemotron: Overview · Developer

Uses

Direct Use

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.

MagpieTTS acts as a dedicated speech-generation layer that plugs into existing AI pipelines without changing upstream language models or downstream audio handling. In cascade voice-agent setups, it converts Large Language Model (LLM) text into natural, real-time speech for user playback. It can also replace or extend existing NVIDIA TTS integrations when multilingual coverage from a single unified model is required.

Deployment Geography: Global

Out-of-Scope Use

This model is not intended for zero-shot voice cloning, languages outside the 12 supported languages, or use cases that bypass the NVIDIA Open Model License terms. See Technical Limitations & Mitigations for additional constraints.

Model Architecture

Architecture Type: Transformer Encoder, Transformer Decoder, Local Transformer, and Feedforward Layers

Figure 1: MagpieTTS Model Architecture

Network Architecture:

1. Causal Transformer Encoder with 6 layers, learnable positional encoder of length 2048, and 1 Layer Normalization output layer.
2. Causal Transformer Decoder with 12 layers, learnable positional encoder of length 2048, and 1 Layer