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
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