Instructions to use doduy1911/nu-q4-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use doduy1911/nu-q4-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="doduy1911/nu-q4-gguf", 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 doduy1911/nu-q4-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf doduy1911/nu-q4-gguf:Q4_0 # Run inference directly in the terminal: llama-cli -hf doduy1911/nu-q4-gguf:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf doduy1911/nu-q4-gguf:Q4_0 # Run inference directly in the terminal: llama-cli -hf doduy1911/nu-q4-gguf: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 doduy1911/nu-q4-gguf:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf doduy1911/nu-q4-gguf: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 doduy1911/nu-q4-gguf:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf doduy1911/nu-q4-gguf:Q4_0
Use Docker
docker model run hf.co/doduy1911/nu-q4-gguf:Q4_0
- LM Studio
- Jan
- Ollama
How to use doduy1911/nu-q4-gguf with Ollama:
ollama run hf.co/doduy1911/nu-q4-gguf:Q4_0
- Unsloth Studio new
How to use doduy1911/nu-q4-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 doduy1911/nu-q4-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 doduy1911/nu-q4-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for doduy1911/nu-q4-gguf to start chatting
- Docker Model Runner
How to use doduy1911/nu-q4-gguf with Docker Model Runner:
docker model run hf.co/doduy1911/nu-q4-gguf:Q4_0
- Lemonade
How to use doduy1911/nu-q4-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull doduy1911/nu-q4-gguf:Q4_0
Run and chat with the model
lemonade run user.nu-q4-gguf-Q4_0
List all available models
lemonade list
VieNeu-TTS
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! 🙏
Voice Cloning Inference
Reference Voice (Speaker Example):
Input Text:
Trên bầu trời xanh thẳm, những đám mây trắng lửng lờ trôi như những chiếc thuyền nhỏ đang lướt nhẹ theo dòng gió. Dưới mặt đất, cánh đồng lúa vàng rực trải dài tới tận chân trời, những bông lúa nghiêng mình theo từng làn gió.
Generated Output (Cloned Voice):
Installation
git clone https://github.com/pnnbao97/VieNeu-TTS.git
cd VieNeu-TTS
uv sync
Gradio Demo
uv run gradio_app.py
Open your browser at http://127.0.0.1:7860.
Demo Video:
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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