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
1git clone --recursive https://github.com/mudler/magpie-tts.cpp
2cd magpie-tts.cpp
3cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j
4
5./build/examples/cli/magpie-cli say \
6 --model magpie-tts-multilingual-357m-q8_0.gguf \
7 --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.
