Instructions to use openbmb/VoxCPM2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VoxCPM
How to use openbmb/VoxCPM2 with VoxCPM:
import soundfile as sf from voxcpm import VoxCPM model = VoxCPM.from_pretrained("openbmb/VoxCPM2") 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 bf16 voice-cloning benchmark rows across 5 languages
#17
by aufklarer - opened
I included VoxCPM2 bf16 in a local voice-cloning benchmark across English, German, Modern Standard Arabic, Spanish, and Mandarin Chinese:
https://www.soniqo.audio/blog/voice-cloning-benchmarks
VoxCPM2 bf16 had a strong Arabic speaker-match row in this run and fixed the Arabic ASR miss I saw in an earlier int8 pass.
The benchmark uses Google FLEURS references and includes reference audio, generated audio, speaker similarity, WER/CER, generated audio length, and RTF for each row.
This is an engineering benchmark rather than a MOS study, but I wanted to share the VoxCPM2 rows with the model community.