VoxCPM 1.5 LoRA Finetuning on IMDA NSC FEMALE_01

60-second takeaway
VoxCPM produced a strong practical result in this benchmark once we stabilized dataset prep and used a clean inference protocol.
The best balance of quality and stability came from step_0004000 in our run.
Prompted inference can over-copy prompt noise, so prompt quality matters as much as checkpoint choice.

Where this fits

• For founders: VoxCPM is a viable production candidate from this benchmark.
• For engineers: use this page for train recipe, checkpoint pick logic, and inference defaults.

For the series overview matrix, see:

• https://instavar.com/blog/IMDA_NSC_Voice_Cloning_Finetuning_Benchmark_2026

Experiment setup

• Base model: VoxCPM1.5
• Dataset: IMDA NSC FEMALE_01
• Audio prep: resampled to 44.1 kHz for VoxCPM1.5 path
• Hardware: RTX 3090 Ti 24 GB
• Training mode: LoRA fine-tuning

Best checkpoint logic

We tracked validation total loss across steps and selected the strongest zone by both trend and listening:

• Best recorded validation total in this run was at step_0004000.
• Later checkpoints remained usable, but were not consistently better on subjective naturalness.

Audio evidence

Best practical sample (this run)

Settings: no-prompt, no denoiser, long text test.

Failure modes we saw

• Prompted outputs can inherit prompt-room noise strongly.
• Denoisers can clean hiss but also shift timbre and bandwidth perception.
• Long-form outputs are sensitive to prompt clip quality and consistency.

Recommended inference settings

For this exact benchmark setup:

• Start from step_0004000 as default checkpoint.
• Use no-prompt generation first to estimate model prior naturalness.
• Add prompt only when you need stronger speaker lock.
• Use denoiser only when hiss/noise is clearly audible.

Engineer appendix

Key paths from this run