mudler/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).

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