Instructions to use walusungungulube/ghana-tts-36k-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use walusungungulube/ghana-tts-36k-gguf with VoxCPM:

import soundfile as sf
from voxcpm import VoxCPM

model = VoxCPM.from_pretrained("walusungungulube/ghana-tts-36k-gguf")

wav = model.generate(
    text="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly expressive speech.",
    prompt_wav_path=None,      # optional: path to a prompt speech for voice cloning
    prompt_text=None,          # optional: reference text
    cfg_value=2.0,             # LM guidance on LocDiT, higher for better adherence to the prompt, but maybe worse
    inference_timesteps=10,   # LocDiT inference timesteps, higher for better result, lower for fast speed
    normalize=True,           # enable external TN tool
    denoise=True,             # enable external Denoise tool
    retry_badcase=True,        # enable retrying mode for some bad cases (unstoppable)
    retry_badcase_max_times=3,  # maximum retrying times
    retry_badcase_ratio_threshold=6.0, # maximum length restriction for bad case detection (simple but effective), it could be adjusted for slow pace speech
)

sf.write("output.wav", wav, 16000)
print("saved: output.wav")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use walusungungulube/ghana-tts-36k-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf walusungungulube/ghana-tts-36k-gguf:F16
# Run inference directly in the terminal:
llama cli -hf walusungungulube/ghana-tts-36k-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf walusungungulube/ghana-tts-36k-gguf:F16
# Run inference directly in the terminal:
llama cli -hf walusungungulube/ghana-tts-36k-gguf:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf walusungungulube/ghana-tts-36k-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf walusungungulube/ghana-tts-36k-gguf:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf walusungungulube/ghana-tts-36k-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf walusungungulube/ghana-tts-36k-gguf:F16

Use Docker

docker model run hf.co/walusungungulube/ghana-tts-36k-gguf:F16

LM Studio
Jan
Ollama
How to use walusungungulube/ghana-tts-36k-gguf with Ollama:
```
ollama run hf.co/walusungungulube/ghana-tts-36k-gguf:F16
```

Unsloth Studio

How to use walusungungulube/ghana-tts-36k-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for walusungungulube/ghana-tts-36k-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for walusungungulube/ghana-tts-36k-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for walusungungulube/ghana-tts-36k-gguf to start chatting

Docker Model Runner
How to use walusungungulube/ghana-tts-36k-gguf with Docker Model Runner:
```
docker model run hf.co/walusungungulube/ghana-tts-36k-gguf:F16
```

Lemonade

How to use walusungungulube/ghana-tts-36k-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull walusungungulube/ghana-tts-36k-gguf:F16

Run and chat with the model

lemonade run user.ghana-tts-36k-gguf-F16

List all available models

lemonade list

Atomic Chat

ghana-tts-36k — GGUF quantized variants

GGUF conversions of ghananlpcommunity/ghana-tts-36k for use with bluryar/VoxCPM.cpp.

The .gguf files are backend-agnostic — the same file runs on both a CPU-only and a CUDA build of the inference engine. Only the binary you compile differs between CPU and GPU deployment, not the weight file.

Files

File	Quant	AudioVAE	Size	Notes
`ghana-tts-36k-f32.gguf`	F32	original	largest	Reference/max-accuracy baseline
`ghana-tts-36k-f16.gguf`	F16	mixed
`ghana-tts-36k-f16-audiovae-f16.gguf`	F16	f16
`ghana-tts-36k-q8_0.gguf`	Q8_0	mixed		Recommended for CPU
`ghana-tts-36k-q8_0-audiovae-f16.gguf`	Q8_0	f16		Recommended for GPU (CUDA)
`ghana-tts-36k-q4_k.gguf`	Q4_K	mixed		Smallest, more accuracy loss
`ghana-tts-36k-q4_k-audiovae-f16.gguf`	Q4_K	f16	smallest of the practical options	Fastest CPU model-only RTF; good compact CUDA option too

Which one to use

Based on bluryar/VoxCPM.cpp's own published benchmarks on the same "voxcpm" architecture family (not run on this exact checkpoint — validate on your own audio before committing to one in production):

CPU inference:

Default pick: ghana-tts-36k-q8_0.gguf — best full-pipeline RTF at this model scale on CPU.
Smaller/faster, slight accuracy trade-off: ghana-tts-36k-q4_k-audiovae-f16.gguf.

GPU (CUDA) inference:

Default pick: ghana-tts-36k-q8_0-audiovae-f16.gguf — best full-pipeline RTF at this model scale on CUDA.
Smallest CUDA-friendly option: ghana-tts-36k-q4_k-audiovae-f16.gguf.

Inference

This repo includes two scripts that build and run VoxCPM.cpp against these weights:

infer_cpu.sh — builds a CPU-only voxcpm_tts and runs it against ghana-tts-36k-q8_0.gguf.
infer_gpu.sh — builds a CUDA-enabled voxcpm_tts (requires an NVIDIA GPU + CUDA toolkit) and runs it against ghana-tts-36k-q8_0-audiovae-f16.gguf.

Usage (either script):

bash infer_cpu.sh "Text to synthesize" prompt.wav "Exact transcript of prompt.wav" out.wav
bash infer_gpu.sh "Text to synthesize" prompt.wav "Exact transcript of prompt.wav" out.wav

Swap in a different .gguf from this repo by editing the MODEL_PATH variable at the top of either script.

Downloads last month: 91

GGUF

Model size

0.7B params

Architecture

llama

Hardware compatibility

8-bit

16-bit

32-bit

Model tree for walusungungulube/ghana-tts-36k-gguf

Base model

ghananlpcommunity/ghana-tts-36k

Quantized

(2)

this model