Instructions to use phucpx247/vieneu-tts-v2-turbo-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use phucpx247/vieneu-tts-v2-turbo-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="phucpx247/vieneu-tts-v2-turbo-gguf",
	filename="vieneu-tts-v2-turbo.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 phucpx247/vieneu-tts-v2-turbo-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf phucpx247/vieneu-tts-v2-turbo-gguf
# Run inference directly in the terminal:
llama-cli -hf phucpx247/vieneu-tts-v2-turbo-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf phucpx247/vieneu-tts-v2-turbo-gguf
# Run inference directly in the terminal:
llama-cli -hf phucpx247/vieneu-tts-v2-turbo-gguf

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 phucpx247/vieneu-tts-v2-turbo-gguf
# Run inference directly in the terminal:
./llama-cli -hf phucpx247/vieneu-tts-v2-turbo-gguf

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 phucpx247/vieneu-tts-v2-turbo-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf phucpx247/vieneu-tts-v2-turbo-gguf

Use Docker

docker model run hf.co/phucpx247/vieneu-tts-v2-turbo-gguf

LM Studio
Jan
Ollama
How to use phucpx247/vieneu-tts-v2-turbo-gguf with Ollama:
```
ollama run hf.co/phucpx247/vieneu-tts-v2-turbo-gguf
```

Unsloth Studio new

How to use phucpx247/vieneu-tts-v2-turbo-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 phucpx247/vieneu-tts-v2-turbo-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 phucpx247/vieneu-tts-v2-turbo-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for phucpx247/vieneu-tts-v2-turbo-gguf to start chatting

Docker Model Runner
How to use phucpx247/vieneu-tts-v2-turbo-gguf with Docker Model Runner:
```
docker model run hf.co/phucpx247/vieneu-tts-v2-turbo-gguf
```

Lemonade

How to use phucpx247/vieneu-tts-v2-turbo-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull phucpx247/vieneu-tts-v2-turbo-gguf

Run and chat with the model

lemonade run user.vieneu-tts-v2-turbo-gguf-{{QUANT_TAG}}

List all available models

lemonade list

🦜 VieNeu-TTS v2 Turbo — GGUF

Ultra-fast Vietnamese & English TTS — runs entirely on CPU, no GPU required.

📖 Model Description

VieNeu-TTS v2 Turbo is the lightweight, CPU-optimized edition of the VieNeu-TTS family — a state-of-the-art Vietnamese Text-to-Speech system. Quantized to GGUF format and paired with an ONNX neural codec, this model delivers near-real-time speech synthesis on commodity hardware: laptops, edge devices, and even Raspberry Pi class machines.

This repository hosts the GGUF quantized weights intended for use with llama-cpp-python as the inference backend, alongside the companion ONNX codec for waveform generation.

What makes it special?

🇻🇳🇺🇸 Bilingual (Code-switching): Naturally handles mixed Vietnamese–English sentences, powered by sea-g2p. No need to pre-label language boundaries.
⚡ Extreme Speed: Optimized GGUF quantization achieves real-time or faster inference on a standard CPU.
💻 Zero GPU Dependency: Runs fully offline on any x86_64 / ARM64 machine with sufficient RAM.
🔇 AI Watermarking: Audio output embeds an imperceptible identifier for responsible AI content tracing.
🔊 24 kHz Audio: High-fidelity waveform output suitable for production applications.

🗂️ Repository Contents

File	Description
`vieneu-v2-turbo-*.gguf`	GGUF quantized LLM backbone (multiple quant levels)

🚀 Quickstart

Option 1 — Install via `vieneu` SDK (Recommended)

# Minimal installation (Turbo/CPU Only)
pip install vieneu

# Optional: Pre-built llama-cpp-python for CPU (if building fails)
pip install vieneu --extra-index-url https://pnnbao97.github.io/llama-cpp-python-v0.3.16/cpu/

# Optional: macOS Metal acceleration
pip install vieneu --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal/

from vieneu import Vieneu

# Turbo mode is the default — no GPU needed
tts = Vieneu()

# Vietnamese only
audio = tts.infer(text="Xin chào! Đây là VieNeu TTS phiên bản Turbo.")
tts.save(audio, "output.wav")

# Bilingual code-switching
audio = tts.infer(
    text="Trước đây, hệ thống điện sử dụng direct current, nhưng Tesla đã chứng minh alternating current is more efficient."
)
tts.save(audio, "output_bilingual.wav")

Option 2 — Web UI (Full repo)

git clone https://github.com/pnnbao97/VieNeu-TTS.git
cd VieNeu-TTS
uv sync          # minimal install (Turbo/CPU)
uv run vieneu-web
# → Open http://127.0.0.1:7860

🌐 Bilingual Code-Switching

VieNeu-TTS v2 Turbo can handle natural Vietnamese–English mixed text without any special markup. The sea-g2p engine automatically identifies language boundaries and generates accurate phonemes for both languages.

from vieneu import Vieneu

tts = Vieneu()

examples = [
    "Hôm nay tôi sẽ trình bày về machine learning và deep learning.",
    "The new feature là rất hữu ích cho developers.",
    "VieNeu supports both Vietnamese và English seamlessly.",
]

for i, text in enumerate(examples):
    audio = tts.infer(text=text)
    tts.save(audio, f"bilingual_{i}.wav")

🎙️ Preset Voices

The model ships with multiple preset voices. List and use them via the SDK:

from vieneu import Vieneu

tts = Vieneu()

# List available preset voices
voices = tts.list_preset_voices()
for description, voice_id in voices:
    print(f"  {description} → ID: {voice_id}")

# Use a specific voice
voice_data = tts.get_preset_voice("xuan_vinh")   # default: Southern Male
audio = tts.infer(
    text="Giọng đọc này được tổng hợp bởi VieNeu Turbo.",
    voice=voice_data
)
tts.save(audio, "preset_voice.wav")

Note: Instant Voice Cloning is not yet available in Turbo mode. It is planned for a future release. For cloning, use the standard GPU-based VieNeu-TTS-v2 model.

🔬 Model Architecture

VieNeu-TTS v2 Turbo is a two-stage TTS system:

LLM Backbone (GGUF): A transformer language model conditioned on text tokens and speaker embeddings. It predicts discrete audio codec tokens autoregressively.
Neural Codec (ONNX): A VQ-VAE-based neural codec (VieNeu-Codec) decodes the predicted token sequence into a 24 kHz waveform.

The bilingual capability is enabled by sea-g2p, which converts mixed-language graphemes to phonemes before the LLM backbone processes them.

📊 Training Data

The model was trained on over 20,000 hours of combined Vietnamese and English speech data, covering a wide range of speakers, accents, recording conditions, and speaking styles.

Dataset	Language	Description
`pnnbao-ump/VieNeu-TTS-1000h`	Vietnamese	Curated studio-quality Vietnamese speech corpus
`pnnbao-ump/vietnamese-audio-corpus`	Vietnamese	Diverse multi-speaker Vietnamese audio
`amphion/Emilia-Dataset`	Multilingual	Large-scale multilingual speech dataset
`facebook/multilingual_librispeech`	English + others	Multilingual read speech

🗺️ Roadmap

GGUF/ONNX Turbo engine
Bilingual (Vietnamese–English) code-switching
Turbo Voice Cloning
Mobile SDK (Android / iOS)
Streaming output API

🤝 Related Resources

Resource	Link
📦 PyPI Package	`pip install vieneu`
🐙 GitHub	pnnbao97/VieNeu-TTS
📖 Documentation	docs.vieneu.io
🤗 Full Model (GPU)	pnnbao-ump/VieNeu-TTS
💬 Discord Community	Join here
☕ Support the project	buymeacoffee.com/pnnbao

📄 License

This model is released under the Apache License 2.0 — free for personal and commercial use.

Made with ❤️ for the Vietnamese TTS community by @pnnbao97 and contributors.

Downloads last month: 28

GGUF

Model size

0.1B params

Architecture

qwen3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

phucpx247
/

vieneu-tts-v2-turbo-gguf

🦜 VieNeu-TTS v2 Turbo — GGUF

📖 Model Description

What makes it special?

🗂️ Repository Contents

🚀 Quickstart

Option 1 — Install via `vieneu` SDK (Recommended)

Option 2 — Web UI (Full repo)

🌐 Bilingual Code-Switching

🎙️ Preset Voices

🔬 Model Architecture

📊 Training Data

🗺️ Roadmap

🤝 Related Resources

📄 License

Datasets used to train phucpx247/vieneu-tts-v2-turbo-gguf

🦜 VieNeu-TTS v2 Turbo — GGUF

📖 Model Description

What makes it special?

🗂️ Repository Contents

🚀 Quickstart

Option 1 — Install via vieneu SDK (Recommended)

Option 2 — Web UI (Full repo)

🌐 Bilingual Code-Switching

🎙️ Preset Voices

🔬 Model Architecture

📊 Training Data

🗺️ Roadmap

🤝 Related Resources

📄 License

Datasets used to train phucpx247/vieneu-tts-v2-turbo-gguf

Option 1 — Install via `vieneu` SDK (Recommended)