---
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
base_model: nvidia/magpie_tts_multilingual_357m
pipeline_tag: text-to-speech
tags:
- gguf
- ggml
- tts
- text-to-speech
- magpie-tts.cpp
- localai
language:
- en
- es
- de
- fr
- it
- pt
- hi
- vi
- ko
- ar
---

# 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).

## 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](https://github.com/mudler/magpie-tts.cpp/blob/main/docs/quantization.md).

## Usage

```bash
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](https://github.com/mudler/magpie-tts.cpp/blob/main/benchmarks/BENCHMARK.md).

## License

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).