Text-to-Speech
VoxCPM
Safetensors
tts
multilingual
voice-cloning
nvfp4
gptq
quantized
8-bit precision
compressed-tensors
Instructions to use ShinePixelOrg/VoxCPM2-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VoxCPM
How to use ShinePixelOrg/VoxCPM2-NVFP4 with VoxCPM:
import soundfile as sf from voxcpm import VoxCPM model = VoxCPM.from_pretrained("ShinePixelOrg/VoxCPM2-NVFP4") 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
Commit History
Yesterday
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GPTQ-calibrated NVFP4: 180 LM matrices, v_proj and down_proj kept BF16
Verified signatureNVFP4 weight-only GPTQ on BaseLM and ResidualLM q/k/o/gate/up; attention V and FFN down stay BF16; acoustic stack untouched.073718e -
Publish weight-only NVFP4 of both language models.
Verified signatureReplaces the earlier partial W4A4 checkpoint. 252 BaseLM and ResidualLM linears are NVFP4 weights with BF16 activations. LocDiT, LocEnc, projections, the stop head, and AudioVAE stay at original precision. The model card records size, the five-sentence quality comparison, and the paired single-stream and 64-way speed against BF16.3bbe57e -
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