magpie-tts.cpp-gguf

**Brought to you by the [LocalAI](https://github.com/mudler/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](https://huggingface.co/nvidia/magpie_tts_multilingual_357m) for [magpie-tts.cpp](https://github.com/mudler/magpie-tts.cpp), a from-scratch C++17/[ggml](https://github.com/ggml-org/ggml) inference engine. Each file bundles the TTS model, the [NanoCodec](https://huggingface.co/nvidia/nemo-nano-codec-22khz-1.89kbps-21.5fps) 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). Quantization is selective (only matmul weights; codec and embeddings stay f32). Full drift numbers and methodology: [docs/quantization.md](https://github.com/mudler/magpie-tts.cpp/blob/main/docs/quantization.md). 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 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](https://github.com/mudler/magpie-tts.cpp/blob/main/benchmarks/BENCHMARK.md). Inference code: MIT. Model weights (these GGUFs): [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Model and codec by NVIDIA (NeMo team). Built by the [LocalAI](https://github.com/mudler/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](https://github.com/mudler/LocalAI).