mudler / magpie-tts.cpp-gguf

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Total runs: 2.5K
24-hour runs: -21
7-day runs: -102
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Model's Last Updated: July 25 2026
text-to-speech

Introduction of magpie-tts.cpp-gguf

Model Details of magpie-tts.cpp-gguf

magpie-tts.cpp GGUF

Brought to you by the LocalAI team , the folks behind LocalAI, the open-source AI engine that runs any model (LLMs, vision, voice, image, video) on any hardware, no GPU required.

Self-contained GGUF builds of NVIDIA's Magpie TTS Multilingual 357M for magpie-tts.cpp , a from-scratch C++17/ ggml inference engine. Each file bundles the TTS model, the NanoCodec decoder, the tokenizer and the G2P dictionaries: one file, no Python, PyTorch, NeMo, or CUDA toolkit at inference.

5 voices (Aria, Jason, John, Leo, Sofia), 22.05 kHz mono, 9+ languages (en, es, de, fr, it, pt-BR, hi, vi, ko, ar variants; zh/ja not yet supported by the C++ tokenizer).

Files
File Size Notes
magpie-tts-multilingual-357m-f32.gguf 1294 MB lossless, parity reference (teacher-forced replay max abs diff 3.6e-5 vs NeMo)
magpie-tts-multilingual-357m-f16.gguf 784 MB ASR round-trip exact
magpie-tts-multilingual-357m-q8_0.gguf 624 MB recommended; ASR round-trip exact, ~1.6x faster decode than f32
magpie-tts-multilingual-357m-q6_k.gguf 584 MB ASR round-trip exact
magpie-tts-multilingual-357m-q5_k.gguf 562 MB ASR round-trip exact on the smoke set; larger logit drift, expect degradation on hard material
magpie-tts-multilingual-357m-q4_k.gguf 541 MB smallest; same caveat as q5_k

Quantization is selective (only matmul weights; codec and embeddings stay f32). Full drift numbers and methodology: docs/quantization.md .

Usage
git clone --recursive https://github.com/mudler/magpie-tts.cpp
cd magpie-tts.cpp
cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j

./build/examples/cli/magpie-cli say \
  --model magpie-tts-multilingual-357m-q8_0.gguf \
  --text "Hello world!" --lang en --speaker Aria --output hello.wav
Performance

About 66x faster than the NeMo reference pipeline on the same CPU (Ryzen 9 9950X3D: 12.1 s vs 805.7 s for ~4 s of speech, f32). Honest methodology and caveats: benchmarks/BENCHMARK.md .

License

Inference code: MIT. Model weights (these GGUFs): NVIDIA Open Model License . Model and codec by NVIDIA (NeMo team).


Built by the LocalAI team. If you want to run text to speech (and LLMs, vision, voice, image, and video models) locally on any hardware with an OpenAI-compatible API, give LocalAI a star .

Runs of mudler magpie-tts.cpp-gguf on huggingface.co

2.5K
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24-hour runs
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3-day runs
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7-day runs
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30-day runs

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Updated:June 22 2026