from voxcpm import VoxCPM
from huggingface_hub import snapshot_download
import soundfile as sf

# Download LoRA
lora_path = snapshot_download("aisyahsyihab/voxcpm-lora-indonesian-female-v2")

# Load model with LoRA
model = VoxCPM.from_pretrained(
    "openbmb/VoxCPM1.5",
    lora_weights_path=lora_path,
    load_denoiser=False
)

# Generate speech
audio = model.generate(
    text="Halo, apa kabar hari ini?",
    cfg_value=2.5,
    normalize=True
)

# Save output
sf.write("output.wav", audio, model.tts_model.sample_rate)

Training Configuration

yaml

lora:
  enable_lm: true
  enable_dit: true
  enable_proj: false
  r: 64
  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"]

training:
  batch_size: 4
  grad_accum_steps: 4
  learning_rate: 0.0001
  warmup_steps: 100
  max_steps: 4000

Hardware Requirements

Training: NVIDIA RTX 5090 (32GB VRAM)
Inference: ~6GB VRAM (with base model)

Inference Tips

CFG value: 2.0-3.0 (higher = more adherence to voice, lower = more natural)
Best for: Casual Indonesian speech, conversational tone
Optimal text length: 1-3 sentences at a time

Changelog

v2 (2026-03-18): Higher capacity LoRA (r=64), 400 samples, loss 0.750
v1 (2026-02-09): Initial release, r=32, 112 samples, loss 0.790

License

Apache 2.0 (following VoxCPM base model license)

Acknowledgments

OpenBMB for VoxCPM 1.5
Training conducted with VoxCPM's official LoRA training pipeline