Instructions to use Apurba-NSU-RnD-Lab/MenoChat_Vox_TTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VoxCPM
How to use Apurba-NSU-RnD-Lab/MenoChat_Vox_TTS with VoxCPM:
import soundfile as sf from voxcpm import VoxCPM model = VoxCPM.from_pretrained("Apurba-NSU-RnD-Lab/MenoChat_Vox_TTS") 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
- Xet hash:
- da76edfe7f7552c88f8c60b697e061b3146a0ed34ac3398392099175b7eaa0b3
- Size of remote file:
- 301 MB
- SHA256:
- 5b93f34c771281679b0ff93be3d1c1681eb1f301c3892e701db8f10a725b20a9
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