Instructions to use thanhtantran/VieNeu-TTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thanhtantran/VieNeu-TTS with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thanhtantran/VieNeu-TTS",
	filename="VieNeu-TTS-q4_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use thanhtantran/VieNeu-TTS with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thanhtantran/VieNeu-TTS:Q4_0
# Run inference directly in the terminal:
llama-cli -hf thanhtantran/VieNeu-TTS:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thanhtantran/VieNeu-TTS:Q4_0
# Run inference directly in the terminal:
llama-cli -hf thanhtantran/VieNeu-TTS:Q4_0

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 thanhtantran/VieNeu-TTS:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf thanhtantran/VieNeu-TTS:Q4_0

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 thanhtantran/VieNeu-TTS:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thanhtantran/VieNeu-TTS:Q4_0

Use Docker

docker model run hf.co/thanhtantran/VieNeu-TTS:Q4_0

LM Studio
Jan
Ollama
How to use thanhtantran/VieNeu-TTS with Ollama:
```
ollama run hf.co/thanhtantran/VieNeu-TTS:Q4_0
```

Unsloth Studio new

How to use thanhtantran/VieNeu-TTS 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 thanhtantran/VieNeu-TTS 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 thanhtantran/VieNeu-TTS to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for thanhtantran/VieNeu-TTS to start chatting

Docker Model Runner
How to use thanhtantran/VieNeu-TTS with Docker Model Runner:
```
docker model run hf.co/thanhtantran/VieNeu-TTS:Q4_0
```

Lemonade

How to use thanhtantran/VieNeu-TTS with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thanhtantran/VieNeu-TTS:Q4_0

Run and chat with the model

lemonade run user.VieNeu-TTS-Q4_0

List all available models

lemonade list

Configuration Parsing Warning:Invalid JSON for config file tokenizer_config.json

Overview

VieNeu-TTS is an advanced on-device Vietnamese Text-to-Speech (TTS) model with instant voice cloning.

Trained on ~1000 hours of high-quality Vietnamese speech, this model represents a significant upgrade from VieNeu-TTS-140h with the following improvements:

Enhanced pronunciation: More accurate and stable Vietnamese pronunciation
Code-switching support: Seamless transitions between Vietnamese and English
Better voice cloning: Higher fidelity and speaker consistency
Real-time synthesis: 24 kHz waveform generation on CPU or GPU

VieNeu-TTS-1000h delivers production-ready speech synthesis fully offline.

Author: Phạm Nguyễn Ngọc Bảo

Support This Project

Training high-quality TTS models requires significant GPU resources and compute time. If you find this model useful, please consider supporting the development:

Your support helps maintain and improve VieNeu-TTS! 🙏

Reference Voices

File	Gender	Accent	Description
Bình (nam miền Bắc)	Male	North	Male voice, North accent
Tuyên (nam miền Bắc)	Male	North	Male voice, North accent
Nguyên (nam miền Nam)	Male	South	Male voice, South accent
Sơn (nam miền Nam)	Male	South	Male voice, South accent
Vĩnh (nam miền Nam)	Male	South	Male voice, South accent
Hương (nữ miền Bắc)	Female	North	Female voice, North accent
Ly (nữ miền Bắc)	Female	North	Female voice, North accent
Ngọc (nữ miền Bắc)	Female	North	Female voice, North accent
Đoan (nữ miền Nam)	Female	South	Female voice, South accent
Dung (nữ miền Nam)	Female	South	Female voice, South accent

Model Architecture

Component	Description
Backbone	Qwen 0.5B (chat-format LM)
Codec	NeuCodec (supports ONNX + quantization)
Output	24 kHz waveform synthesis
Context Window	2048 tokens shared text + speech
Watermark	Enabled
Training Data	VieNeuCodec-dataset + Emilia dataset pretraining

Features

High-quality Vietnamese speech
Instant voice cloning (3–5 second reference audio)
Fully offline
Runs real-time or faster
Multi-voice reference support
Python API + CLI + Gradio

Troubleshooting

Issue	Cause	Solution
Missing `libespeak`	System dependency	Install eSpeak NG
GPU OOM	VRAM too small	Use CPU or quantized model
Poor voice match	Bad reference sample	Try a clearer reference clip

License

Apache 2.0

Citation

@misc{vieneutts2025,
  title        = {VieNeu-TTS: Vietnamese Text-to-Speech with Instant Voice Cloning},
  author       = {Pham Nguyen Ngoc Bao},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/pnnbao-ump/VieNeu-TTS}}
}

Please also cite the base model:

@misc{neuttsair2025,
  title        = {NeuTTS Air: On-Device Speech Language Model with Instant Voice Cloning},
  author       = {Neuphonic},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/neuphonic/neutts-air}}
}

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Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for thanhtantran/VieNeu-TTS

Base model

neuphonic/neutts-air

Quantized

(7)

this model

thanhtantran
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VieNeu-TTS