magpie-tts.cpp GGUF
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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).
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