Instructions to use pnnbao-ump/VieNeu-TTS-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pnnbao-ump/VieNeu-TTS-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pnnbao-ump/VieNeu-TTS-v2",
	filename="VieNeu-TTS-v2-Q4-K-M.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 pnnbao-ump/VieNeu-TTS-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pnnbao-ump/VieNeu-TTS-v2
# Run inference directly in the terminal:
llama-cli -hf pnnbao-ump/VieNeu-TTS-v2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pnnbao-ump/VieNeu-TTS-v2
# Run inference directly in the terminal:
llama-cli -hf pnnbao-ump/VieNeu-TTS-v2

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 pnnbao-ump/VieNeu-TTS-v2
# Run inference directly in the terminal:
./llama-cli -hf pnnbao-ump/VieNeu-TTS-v2

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 pnnbao-ump/VieNeu-TTS-v2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pnnbao-ump/VieNeu-TTS-v2

Use Docker

docker model run hf.co/pnnbao-ump/VieNeu-TTS-v2

LM Studio
Jan
Ollama
How to use pnnbao-ump/VieNeu-TTS-v2 with Ollama:
```
ollama run hf.co/pnnbao-ump/VieNeu-TTS-v2
```

Unsloth Studio new

How to use pnnbao-ump/VieNeu-TTS-v2 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 pnnbao-ump/VieNeu-TTS-v2 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 pnnbao-ump/VieNeu-TTS-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pnnbao-ump/VieNeu-TTS-v2 to start chatting

Docker Model Runner
How to use pnnbao-ump/VieNeu-TTS-v2 with Docker Model Runner:
```
docker model run hf.co/pnnbao-ump/VieNeu-TTS-v2
```

Lemonade

How to use pnnbao-ump/VieNeu-TTS-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pnnbao-ump/VieNeu-TTS-v2

Run and chat with the model

lemonade run user.VieNeu-TTS-v2-{{QUANT_TAG}}

List all available models

lemonade list

Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)

🦜 VieNeu-TTS

Overview

VieNeu-TTS-v2 is the next generation of Vietnamese TTS, designed for Natural Communication, Podcasts, and Bilingual (En-Vi) Code-switching.

What's new in V2:

10,000+ Hours Data: Trained on a massive bilingual dataset for unparalleled naturalness.

Multi-Speaker Conversation: Support for podcast-style scripts with distinct voices and emotional nuances.

Seamless Code-switching: High-quality English integration within Vietnamese sentences.

Instant Voice Cloning: Still supports cloning with just 3-5 seconds of audio.

This project features the flagship VieNeu-TTS-v2 architecture:

VieNeu-TTS-v2 (0.3B): Optimized for high-fidelity bilingual speech and long-form content.
VieNeu-TTS-v2-Turbo: Optimized for ultra-low latency and CPU deployment using GGUF.

Tác giả: Phạm Nguyễn Ngọc Bảo

☕ Support This Project

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

🔥 Quick Start (Web UI)

git clone https://github.com/pnnbao97/VieNeu-TTS.git
cd VieNeu-TTS

# Install uv (if you haven't)
# Windows: powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
# Linux/macOS: curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies & Run
uv sync --group gpu
uv run vieneu-web

📦 Using Python SDK (vieneu)

Install the SDK to integrate VieNeu-TTS-0.3B into your research or applications:

# Windows (Avoid llama-cpp build errors)
pip install vieneu --extra-index-url https://pnnbao97.github.io/llama-cpp-python-v0.3.16/cpu/

# Linux / MacOS
pip install vieneu

Full Features Guide

from vieneu import Vieneu

# Initialize in Standard mode (Default - Highest quality)
tts = Vieneu(emotion="natural") # emotion="natural" (giọng tự nhiên - mặc định) hoặc "storytelling" (giọng kể chuyện)

# 1. Simple synthesis (uses default Northern Female voice 'Trúc Ly')
text = "Chào bạn. Tôi là VieNeu-TTS, tôi có thể giúp bạn đọc sách, làm chatbot thời gian thực, thậm chí clone giọng nói của bạn."
audio = tts.infer(text=text)

# Save to file
tts.save(audio, "output_Trúc Ly.wav")
print("💾 Saved to output_Trúc Ly.wav")

# 2. Using a specific Preset Voice
voices = tts.list_preset_voices()
for desc, voice_id in voices:
    print(f"Voice: {desc} (ID: {voice_id})")

my_voice_id = voices[1][1] if len(voices) > 1 else voices[0][1] # Giọng Phạm Tuyên
voice_data = tts.get_preset_voice(my_voice_id)

audio_custom = tts.infer(text="Tôi đang nói bằng giọng của Bác sĩ Tuyên.", voice=voice_data)

# 3. Save to file
tts.save(audio_custom, "output_Phạm Tuyên.wav")
print("💾 Saved to output_Phạm Tuyên.wav")

Remote Mode (Ultra-Fast with LMDeploy Server)

Deploy VieNeu-TTS as a high-performance API Server (powered by LMDeploy) with a single command.

1. Run with Docker (Recommended)

Requirement: NVIDIA Container Toolkit is required for GPU support.

Start the Server with a Public Tunnel (No port forwarding needed):

docker run --gpus all -p 23333:23333 -v huggingface_cache:/root/.cache/huggingface pnnbao/vieneu-tts:latest --tunnel

Default: The server loads the VieNeu-TTS-v2 model for maximum quality.
Tunneling: The Docker image includes a built-in bore tunnel. Check the container logs to find your public address (e.g., bore.pub:31631).

2. Using the SDK (Remote Mode)

Once the server is running, you can connect from anywhere (Colab, Web Apps, etc.) without loading heavy models locally.

Installation:

pip install "vieneu[gpu]"

Usage:

from vieneu import Vieneu
import os

# Configuration
REMOTE_API_BASE = 'http://your-server-ip:23333/v1'  # Or bore tunnel URL
REMOTE_MODEL_ID = "pnnbao-ump/VieNeu-TTS-v2"

# Initialization (LIGHTWEIGHT - only loads small codec locally)
# Default emotion is "natural" (conversational) - set emotion="storytelling" for storytelling mode
tts = Vieneu(mode='remote', api_base=REMOTE_API_BASE, model_name=REMOTE_MODEL_ID, emotion="natural")
os.makedirs("outputs", exist_ok=True)

# List remote voices
available_voices = tts.list_preset_voices()
for desc, name in available_voices:
    print(f"   - {desc} (ID: {name})")

# Use specific voice (dynamically select second voice)
if available_voices:
    _, my_voice_id = available_voices[1]
    voice_data = tts.get_preset_voice(my_voice_id)
    audio_spec = tts.infer(text="Chào bạn, tôi đang nói bằng giọng của bác sĩ Tuyên.", voice=voice_data)
    tts.save(audio_spec, f"outputs/remote_{my_voice_id}.wav")
    print(f"💾 Saved synthesis to: outputs/remote_{my_voice_id}.wav")

# Standard synthesis (uses default voice)
text_input = "Chế độ remote giúp tích hợp VieNeu vào ứng dụng Web hoặc App cực nhanh mà không cần GPU tại máy khách."
audio = tts.infer(text=text_input)
tts.save(audio, "outputs/remote_output.wav")
print("💾 Saved remote synthesis to: outputs/remote_output.wav")

# Zero-shot voice cloning (encodes audio locally, sends codes to server)
if os.path.exists("examples/audio_ref/example_ngoc_huyen.wav"):
    cloned_audio = tts.infer(
        text="Đây là giọng nói được clone và xử lý thông qua VieNeu Server.",
        ref_audio="examples/audio_ref/example_ngoc_huyen.wav",
        ref_text="Tác phẩm dự thi bảo đảm tính khoa học, tính đảng, tính chiến đấu, tính định hướng."
    )
    tts.save(cloned_audio, "outputs/remote_cloned_output.wav")
    print("💾 Saved remote cloned voice to: outputs/remote_cloned_output.wav")

📋 Reference Voices

File	Gender	Accent	Description
Bình	Male	North	Male voice, North accent
Tuyên	Male	North	Male voice, North accent
Nguyên	Male	South	Male voice, South accent
Hương	Female	North	Female voice, North accent
Ngọc	Female	North	Female voice, North accent
Đoan	Female	South	Female voice, South accent

🔬 Model Variants

Model	Format	Device	Quality	Features
VieNeu-TTS-v2	PyTorch	GPU/CPU	⭐⭐⭐⭐⭐	Podcast, En-Vi CS
VieNeu-TTS-v2 (GGUF)	GGUF Q4	CPU	⭐⭐⭐⭐	Fastest CPU, Podcast
VieNeu-TTS-v1	PyTorch	GPU	⭐⭐⭐⭐	Stable (Vi only)
VieNeu-TTS-0.3B	PyTorch	GPU/CPU	⭐⭐⭐	Legacy Ultra-Fast

📑 Citation

@misc{vieneutts2026,
  title        = {VieNeu-TTS-v2: Vietnamese Text-to-Speech with Instant Voice Cloning},
  author       = {Pham Nguyen Ngoc Bao},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/pnnbao-ump/VieNeu-TTS}}
}

Made with ❤️ for the Vietnamese TTS community

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Safetensors

Model size

0.3B params

Tensor type

BF16

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Collection including pnnbao-ump/VieNeu-TTS-v2

VieNeu-TTS-v2

Collection

VieNeu-TTS-v2 is an advanced on-device Vietnamese Text-to-Speech (TTS) model with instant voice cloning and English-Vietnamese bilingual support. • 5 items • Updated 20 days ago • 2