Instructions to use chandrashekars/VoxCPM2-Indic-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VoxCPM
How to use chandrashekars/VoxCPM2-Indic-LoRA with VoxCPM:
import soundfile as sf from voxcpm import VoxCPM model = VoxCPM.from_pretrained("chandrashekars/VoxCPM2-Indic-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
VoxCPM2 Fine-tuned for Indic Languages
Fine-tuned from openbmb/VoxCPM2 on Indic language TTS data.
Languages
- Hindi
Training Details
- Mode: lora (LoRA r=32)
- Epochs: 5
- Learning Rate: 0.0001
- Datasets: SPRINGLab/IndicVoices-R_Hindi
Usage
from voxcpm import VoxCPM
model = VoxCPM.from_pretrained(
"chandrashekars/VoxCPM2-Indic-LoRA",
load_denoiser=False,
)
# Generate Hindi speech
wav = model.generate(
text="नमस्ते, यह VoxCPM2 है।",
cfg_value=2.0,
inference_timesteps=10,
)
import soundfile as sf
sf.write("output.wav", wav, model.tts_model.sample_rate)
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Model tree for chandrashekars/VoxCPM2-Indic-LoRA
Base model
openbmb/VoxCPM2