Instructions to use hetanshwaghela/amnesiac-voxcpm-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VoxCPM
How to use hetanshwaghela/amnesiac-voxcpm-lora with VoxCPM:
import soundfile as sf from voxcpm import VoxCPM model = VoxCPM.from_pretrained("hetanshwaghela/amnesiac-voxcpm-lora") 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
| { | |
| "base_model": "openbmb/VoxCPM1.5", | |
| "lora_config": { | |
| "enable_lm": true, | |
| "enable_dit": true, | |
| "enable_proj": false, | |
| "r": 16, | |
| "alpha": 32, | |
| "dropout": 0.0, | |
| "target_modules_lm": [ | |
| "q_proj", | |
| "v_proj", | |
| "k_proj", | |
| "o_proj" | |
| ], | |
| "target_modules_dit": [ | |
| "q_proj", | |
| "v_proj", | |
| "k_proj", | |
| "o_proj" | |
| ], | |
| "target_proj_modules": [ | |
| "enc_to_lm_proj", | |
| "lm_to_dit_proj", | |
| "res_to_dit_proj" | |
| ] | |
| } | |
| } |