**Brought to you by the LocalAI team** — the creators of LocalAI, the open-source AI engine that runs any model — LLMs, vision, voice, image, video — on any hardware. No GPU required. Quantized GGUF weights for vibevoice.cpp, a C/C++ port of Microsoft VibeVoice (TTS + ASR) on top of `ggml`. `scripts/quantize_gguf.py` in the source repo selectively quantizes only the LM matmul weights — attention q/k/v/o, ffn gate/up/down, and lm_head — to Q8_0. Everything else (1-D conv kernels, RMSNorm scales, biases, layer-scale gammas, token embeddings, small scalars) passes through unchanged. The conv1d implementation in vibevoice.cpp casts kernels to F16 inline rather than dequantizing on the fly, so quantizing those would corrupt the convolution outputs.

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voice-detect-gguf

mudler

Each `.gguf` here is a self-contained, metadata-driven model produced by voice-detect.cpp, a standalone C++/ggml engine for speaker recognition and voice analysis. No Python, PyTorch, or ONNX runtime is needed at inference time: the GGUF carries the weights verbatim plus the forward-graph topology in its KV metadata, and the C++ engine replays it. One Kaldi-compatible 80-dim FBank front end feeds every speaker encoder; only the encoder graph changes between models. These models carry **different licenses** depending on their source weights. The four speaker encoders are commercial-friendly; the two wav2vec2 analysis heads are **non-commercial, research-only**. Pick what matches your use case. > The two **wav2vec2 analysis heads** (emotion, age/gender) are redistributed under > their upstream **CC-BY-NC-SA-4.0** terms and are for **research / non-commercial use > only**. The four speaker encoders (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++) are > commercial-friendly.

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