VieNeu-TTS-v2

VieNeu-TTS

Overview

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

[!IMPORTANT] 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

Model Variants

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