FarmerlineML/voxcpm2-krio-sft

VoxCPM2 Krio — Full SFT

Full fine-tune of VoxCPM2 on Krio (Sierra Leone Creole) speech data. Trained by FarmerlineML for the darli AI agricultural voice assistant.

Training Details

Parameter	Value
Base model	openbmb/VoxCPM2 (2B)
Method	Full SFT (all parameters)
Learning rate	1e-5
Batch size	1 (grad accum 16, effective batch 16)
Sample rate	16kHz (AudioVAE encoder input)
Final step	1484

Validation Loss:

Step	loss/total	loss/diff	loss/stop
0	1.171295	0.762161	0.272756
200	0.780878	0.764130	0.011165
400	0.706954	0.694261	0.008462
600	0.693552	0.691856	0.001131
800	0.636625	0.634136	0.001659
1000	0.704854	0.676256	0.019065
1200	0.679028	0.677448	0.001054
1400	0.710708	0.652015	0.039129

Datasets:

• FarmerlineML/Krio_updated
• FarmerlineML/2026_krio_TTS

Usage

from voxcpm import VoxCPM
import soundfile as sf
import numpy as np

model = VoxCPM.from_pretrained(
    "FarmerlineML/voxcpm2-krio-sft",
    load_denoiser=False,
)

def trim_audio(wav, sr, silence_thresh=0.01, max_silence_secs=2.0):
    abs_wav = np.abs(wav)
    window  = int(0.05 * sr)
    n_wins  = len(abs_wav) // window
    max_sil = int(max_silence_secs / 0.05)
    silence_count, cut_sample = 0, len(wav)
    for w in range(n_wins):
        chunk = abs_wav[w * window:(w + 1) * window]
        if chunk.max() < silence_thresh:
            silence_count += 1
            if silence_count >= max_sil:
                cut_sample = (w - max_sil + 1) * window
                break
        else:
            silence_count = 0
    return wav[:min(cut_sample + int(0.1 * sr), len(wav))]

wav = model.generate(
    text="Ow di bodi? Mi na Farmerline agent.",
    reference_wav_path="your_krio_speaker.wav",
    cfg_value=2.0,
    inference_timesteps=15,
    retry_badcase=False,
    max_len=max(50, len(text) * 4),
)
wav = trim_audio(wav, 48000)
sf.write("output.wav", wav, 48000)

Repo Structure

├── model.safetensors        # Model weights (~9.2GB)
├── audiovae.pth             # AudioVAE decoder
├── config.json              # Model architecture config
├── tokenizer.json           # Tokenizer
├── training/
│   ├── train.log            # Full training log
│   ├── val_loss_summary.txt # Validation losses per checkpoint
│   └── training_state.json  # Final training state
└── tensorboard/             # TensorBoard event files

Notes

• Reference audio is required at inference for voice identity anchoring
• Use max_len=max(50, len(text) * 4) to prevent hallucination after sentence end
• A post-generation 2-second silence trim is strongly recommended
• Trained on Sierra Leone Krio — best results with Krio speaker reference audio