The rapid evolution of Text-to-Speech (TTS) technology has finally reached a milestone for Vietnamese users with VieNeu-TTS, the first-ever Vietnamese TTS model capable of running entirely on personal devices. Fine-tuned from NeuTTS Air, this model brings hyper-realistic, natural Vietnamese voices that are generated instantly and locally, even on mid-range CPUs. At its core lies the Qwen 0.5B LLM backbone, striking a rare balance between speed, compactness, and exceptional sound quality. VieNeu-TTS isn’t just another open-source model; it’s a complete, privacy-first solution designed for real-world use in voice agents, virtual assistants, educational tools, interactive toys, and secure offline applications. With NeuCodec powering its audio generation and efficient formats like Safetensors and GGUF (Q8/Q4) enabling lightweight inference, VieNeu-TTS delivers real-time speech synthesis without draining your system’s power or requiring any GPU setup.
In this guide, you’ll learn how to install and run VieNeu-TTS locally, turning your machine into a fully capable Vietnamese speech generator within minutes. If you’re a developer, AI enthusiast, or hobbyist, this walkthrough will help you experience just how far localized AI voice synthesis has come.
Prerequisites
The minimum system requirements for running this model are:
- GPU: 1x RTX4090 or 1x RTX A6000 (depending on the use case scale)
- Storage: 20 GB (preferable)
- VRAM: at least 8 GB
- Anaconda installed
Step-by-step process to install and run VieNeu-TTS
For the purpose of this tutorial, we’ll use a GPU-powered Virtual Machine by NodeShift since it provides high compute Virtual Machines at a very affordable cost on a scale that meets GDPR, SOC2, and ISO27001 requirements. Also, it offers an intuitive and user-friendly interface, making it easier for beginners to get started with Cloud deployments. However, feel free to use any cloud provider of your choice and follow the same steps for the rest of the tutorial.
Step 1: Setting up a NodeShift Account
Visit app.nodeshift.com and create an account by filling in basic details, or continue signing up with your Google/GitHub account.
If you already have an account, login straight to your dashboard.
Step 2: Create a GPU Node
After accessing your account, you should see a dashboard (see image), now:
- Navigate to the menu on the left side.
- Click on the GPU Nodes option.
- Click on Start to start creating your very first GPU node.
These GPU nodes are GPU-powered virtual machines by NodeShift. These nodes are highly customizable and let you control different environmental configurations for GPUs ranging from H100s to A100s, CPUs, RAM, and storage, according to your needs.
Step 3: Selecting configuration for GPU (model, region, storage)
- For this tutorial, we’ll be using 1x RTX A6000 GPU, however, you can choose any GPU as per the prerequisites.
- Similarly, we’ll opt for 200GB storage by sliding the bar. You can also select the region where you want your GPU to reside from the available ones.
Step 4: Choose GPU Configuration and Authentication method
- After selecting your required configuration options, you’ll see the available GPU nodes in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x RTX A6000 48GB GPU node with 64vCPUs/63GB RAM/200GB SSD.
2. Next, you’ll need to select an authentication method. Two methods are available: Password and SSH Key. We recommend using SSH keys, as they are a more secure option. To create one, head over to our official documentation.
Step 5: Choose an Image
The final step is to choose an image for the VM, which in our case is Nvidia Cuda.
We will switch to the Custom Image tab and select a specific Docker image that meets all runtime and compatibility requirements.
We choose the following image:
nvidia/cuda:12.1.1-devel-ubuntu22.04
This image is essential because it includes:
- Full CUDA toolkit (including
nvcc)
- Proper support for building and running GPU-based applications
- Compatibility with CUDA 12.1.1 required by certain model operations
Launch Mode
We selected:
Interactive shell server
This gives us SSH access and full control over terminal operations — perfect for installing dependencies, running benchmarks, and launching models.
Docker Repository Authentication
We left all fields empty here.
Since the Docker image is publicly available on Docker Hub, no login credentials are required.
Identification
nvidia/cuda:12.1.1-devel-ubuntu22.04
That’s it! You are now ready to deploy the node. Finalize the configuration summary, and if it looks good, click Create to deploy the node.
Step 6: Connect to active Compute Node using SSH
- As soon as you create the node, it will be deployed in a few seconds or a minute. Once deployed, you will see a status Running in green, meaning that our Compute node is ready to use!
- Once your GPU shows this status, navigate to the three dots on the right, click on Connect with SSH, and copy the SSH details that appear.
As you copy the details, follow the below steps to connect to the running GPU VM via SSH:
- Open your terminal, paste the SSH command, and run it.
2. In some cases, your terminal may take your consent before connecting. Enter ‘yes’.
3. A prompt will request a password. Type the SSH password, and you should be connected.
Output:
Next, If you want to check the GPU details, run the following command in the terminal:
!nvidia-smi
Step 7: Set up the project environment with dependencies
- Create a virtual environment with Anaconda.
conda create -n tts python=3.11 -y && conda activate tts
Output:
2. Clone the official repository.
git clone https://github.com/pnnbao97/VieNeu-TTS.git && cd VieNeu-TTS
Output:
3. Install required packages and dependencies.
pip install -r requirements.txt
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
apt install espeak
Output:
Step 8: Download and Run the Model
1. Run the gradio interface.
python gradio_app.py
Output:
2. If you’re on a remote machine (e.g., NodeShift GPU), you’ll need to do SSH port forwarding in order to access the Gradio session on your local browser.
Run the following command in your local terminal after replacing:
<YOUR_SERVER_PORT> with the PORT allotted to your remote server (For the NodeShift server – you can find it in the deployed GPU details on the dashboard).
<PATH_TO_SSH_KEY> with the path to the location where your SSH key is stored.
<YOUR_SERVER_IP> with the IP address of your remote server.
ssh -L 7860:localhost:7860 -p <YOUR_SERVER_PORT> -i <PATH_TO_SSH_KEY> root@<YOUR_SERVER_IP>
After this copy the URL you received in your remote server: http://0.0.0.0:7860
And paste this on your local browser to access the Gradio session.
3. Once you upload an input text and choose a voice the model will synthesize an audio of the chosen voice speaking the given vietnamese text.
Conclusion
VieNeu-TTS represents a breakthrough in localized AI voice technology, merging lightweight architecture, natural Vietnamese speech synthesis, and real-time performance into one accessible open-source model. Through this article, we explored how its Qwen 0.5B backbone, NeuCodec integration, and on-device optimization make it an ideal fit for privacy-focused applications like personal assistants and embedded systems. With NodeShift Cloud, developers can effortlessly deploy, test, and scale VieNeu-TTS in containerized environments, ensuring seamless transitions between local experimentation and cloud-based production setups. Together, they redefine how developers build and serve high-quality Vietnamese TTS experiences, both securely on-device and efficiently in the cloud.
