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
File size: 472 Bytes
3744d75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"base_model": ".",
"lora_config": {
"enable_lm": true,
"enable_dit": true,
"enable_proj": false,
"r": 32,
"alpha": 16,
"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"
]
}
} |