Instructions to use grruwi/VoxCPM2-audiocpp-prebuilt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VoxCPM
How to use grruwi/VoxCPM2-audiocpp-prebuilt with VoxCPM:
import soundfile as sf from voxcpm import VoxCPM model = VoxCPM.from_pretrained("grruwi/VoxCPM2-audiocpp-prebuilt") wav = model.generate( text="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly expressive speech.", prompt_wav_path=None, # optional: path to a prompt speech for voice cloning prompt_text=None, # optional: reference text cfg_value=2.0, # LM guidance on LocDiT, higher for better adherence to the prompt, but maybe worse inference_timesteps=10, # LocDiT inference timesteps, higher for better result, lower for fast speed normalize=True, # enable external TN tool denoise=True, # enable external Denoise tool retry_badcase=True, # enable retrying mode for some bad cases (unstoppable) retry_badcase_max_times=3, # maximum retrying times retry_badcase_ratio_threshold=6.0, # maximum length restriction for bad case detection (simple but effective), it could be adjusted for slow pace speech ) sf.write("output.wav", wav, 16000) print("saved: output.wav") - Notebooks
- Google Colab
- Kaggle
VoxCPM2 — prebuilt weight sets for audio.cpp
VoxCPM2 converted for audio.cpp, cut exactly the way the Voicebox-on-Linux panel splits the model: the language part (BaseLM) and the acoustic part (ResidualLM, LocEnc, DiT, projections) each in its own precision. AudioVAE (the head) is always F32.
| set | BaseLM | acoustic | size |
|---|---|---|---|
q8_0-f16/ |
Q8_0 | F16 | 3.6 GB |
q8_0-f32/ |
Q8_0 | F32 | 5.2 GB |
f16-f16/ |
F16 | F16 | 5.0 GB |
f16-f32/ |
F16 | F32 | 6.6 GB |
Why prebuilt: without them the engine takes the BF16 original and converts it into the chosen precision on every load — slow (~7 s, ~23 s from a cold disk) and RAM can spike hard while both copies sit in memory. A prebuilt set loads in ~2 s.
The tensors are bit for bit what the engine would produce on the fly (same ggml conversion):
generator biases and norms F32, BaseLM matrices → BaseLM type, the rest of the generator →
acoustic type, AudioVAE untouched. Made with audiocpp/make_weights.py from the repo above —
run it yourself if you prefer.
Use
Each set is a directory with voxcpm2.gguf, laid out as Voicebox-on-Linux expects:
models/audiocpp/prebuilt/<set>/voxcpm2.gguf (./download-models.sh all fetches all four).
Tested with the patched audio.cpp from that repo (audiocpp/voicebox.patch, @ 497c7f0).
License
Apache 2.0, as the original openbmb/VoxCPM2.
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Model tree for grruwi/VoxCPM2-audiocpp-prebuilt
Base model
openbmb/VoxCPM2