VoxCPM2 Akan (Asante Twi), Full SFT
Full fine-tune of VoxCPM2 on Akan (Asante Twi) 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 | 2 (grad accum 16, effective batch 32) |
| Sample rate | 16kHz (AudioVAE encoder input) |
| Final step | 3028 |
Validation Loss:
| Step | loss/total | loss/diff | loss/stop |
|---|---|---|---|
| 0 | 0.899851 | 0.799370 | 0.066987 |
| 500 | 0.753233 | 0.704343 | 0.032593 |
| 1000 | 0.691477 | 0.670356 | 0.014081 |
| 1500 | 0.694497 | 0.682667 | 0.007886 |
| 2000 | 0.709386 | 0.686562 | 0.015216 |
| 2500 | 0.691271 | 0.673016 | 0.012170 |
| 3000 | 0.662822 | 0.647756 | 0.010043 |
Datasets:
TODO: confirm exact dataset repo names before publishing, the source manifests referenced FarmerlineML Akan/Twi TTS data but the exact repo IDs need verifying.
Usage
from voxcpm import VoxCPM
import soundfile as sf
import numpy as np
model = VoxCPM.from_pretrained(
"FarmerlineML/voxcpm2-akan-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="Ɛnyɛ asɔrekafoɔ no nkoaa na ɔyɛɛ biribi a ɛte saa",
reference_wav_path="your_akan_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
voxcpm2-akan-sft/
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 Asante Twi audio, use an Akan/Twi speaker reference clip for best results