File	Description
`model.safetensors`	Merged model weights (PyTorch, GPU/CPU)
`VieNeu-TTS-Twi-Q4_K_M.gguf`	Quantized GGUF for fast CPU inference
`voices.json`	Pre-encoded Twi voice presets

Usage

With the VieNeu SDK

Install from the fork that includes Twi language support:

bash

pip install git+https://github.com/michsethowusu/VieNeu-TTS.git
pip install phonemizer
# system dep for phonemizer
sudo apt-get install espeak-ng

GPU inference:

python

from vieneu import Vieneu
import json, soundfile as sf
from huggingface_hub import hf_hub_download

# Load voice presets
voices_path = hf_hub_download("michsethowusu/VieNeu-TTS-Twi", "voices.json")
with open(voices_path) as f:
    voices = json.load(f)

tts = Vieneu(
    mode="standard",
    backbone_repo="michsethowusu/VieNeu-TTS-Twi",
    backbone_device="cuda",
    codec_repo="neuphonic/neucodec-onnx-decoder-int8",
    lang="twi",
    emotion=None,
)

audio = tts.infer(
    "Nanso Petro san hyɛɛ Kristofo nkuran sɛ monni nnipa nyinaa ni.",
    voice=voices["presets"]["twi_voice_0"],
)
sf.write("output.wav", audio, 24000)

CPU inference (GGUF — fast):

python

pip install llama-cpp-python

tts = Vieneu(
    mode="standard",
    backbone_repo="michsethowusu/VieNeu-TTS-Twi",
    backbone_device="cpu",
    gguf_filename="VieNeu-TTS-Twi-Q4_K_M.gguf",
    codec_repo="neuphonic/neucodec-onnx-decoder-int8",
    lang="twi",
    emotion=None,
)

Production API server (GPU):

bash

pip install vieneu[gpu]
vieneu-serve \
  --model michsethowusu/VieNeu-TTS-Twi \
  --model-name michsethowusu/VieNeu-TTS-Twi \
  --port 23333

# Then connect from your app:
tts = Vieneu(
    mode="remote",
    api_base="http://your-server:23333/v1",
    model_name="michsethowusu/VieNeu-TTS-Twi",
    lang="twi",
    emotion=None,
)

Voice presets

The model ships with 5 voice presets (twi_voice_0 through twi_voice_4), all sampled from the training speaker. Load them from voices.json as shown above.

Training details

Setting	Value
Base model	`pnnbao-ump/VieNeu-TTS-0.3B`
Method	LoRA (r=16, α=32)
Dataset	`ghananlpcommunity/asante-twi-bible-speech-text`
Samples	7,000
Steps	5,000
Hardware	A100 40GB
Phonemizer	espeak-ng `lfn` backend
Format	In-context voice cloning

Limitations

Optimised for Asante Twi (Akan). Other dialects may work but are untested.
Voice cloning works best with the included voices.json presets (same speaker as training data). External reference audio may produce lower quality.
Not intended for commercial use (CC BY-NC 4.0).

License

CC BY-NC 4.0 — non-commercial use only. Mention michsethowusu / GhanaNLP when using.

michsethowusu/Akiti-TTS

VieNeu-TTS — Asante Twi

Demo

Model files