Instructions to use thanhtantran/VieNeu-TTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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 Settings
- llama.cpp
How to use thanhtantran/VieNeu-TTS 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 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 serve -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
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
- Atomic Chat new
- 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
File size: 3,753 Bytes
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license: apache-2.0
datasets:
- pnnbao-ump/VieNeu-TTS-1000h
- pnnbao-ump/VieNeuCodec-dataset
- pnnbao-ump/VieNeu-TTS-140h
language:
- vi
base_model:
- neuphonic/neutts-air
pipeline_tag: text-to-speech
---
## 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:
[](https://buymeacoffee.com/pnnbao)
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
```bibtex
@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:
```bibtex
@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}}
}
``` |