When it comes to text-to-speech, most high-quality models demand hefty GPUs, large downloads, and complex setups, making them out of reach for everyday devices. Kitten TTS changes that game entirely. This open-source, ultra-lightweight model provides realistic voice synthesis into just 15 million parameters, with a model size under 25MB. Despite its small footprint, it delivers multiple premium-quality voice options and is optimized for CPU-only deployment, meaning you can run it smoothly on laptops, edge devices, or even low-powered hardware, no dedicated graphics card required. With real-time inference speed and exceptional clarity, Kitten TTS is a perfect fit for developers, hobbyists, and content creators who want professional-grade audio generation without the usual hardware or cloud costs.
In this article, we’re going to see the step-by-step installation process of Kitten TTS.
Prerequisites
The minimum system requirements for running this model are:
- GPU: 1x RTX4090 (or CPU-only would work too)
- Storage: 10 GB (preferable)
- VRAM: at least 8 GB
- Anaconda installed
Step-by-step process to install and run Kitten 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.
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 using Anaconda.
conda create -n kitten python=3.11 -y && conda activate kitten
Output:
2. Once you’re inside the environment, download the model & dependencies.
pip install https://github.com/KittenML/KittenTTS/releases/download/0.1/kittentts-0.1.0-py3-none-any.whl
Output:
3. Install and run jupyter notebook.
conda install -c conda-forge --override-channels notebook -y
conda install -c conda-forge --override-channels ipywidgets -y
jupyter notebook --allow-root
4. If you’re on a remote machine (e.g., NodeShift GPU), you’ll need to do SSH port forwarding in order to access the jupyter notebook 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 8888:localhost:8888 -p <YOUR_SERVER_PORT> -i <PATH_TO_SSH_KEY> root@<YOUR_SERVER_IP>
Output:
After this copy the URL you received in your remote server:
And paste this on your local browser to access the Jupyter Notebook session.
Step 8: Download and Run the model
- Open a Python notebook inside Jupyter.
2. Download the model checkpoints and run the model for inference.
from kittentts import KittenTTS
m = KittenTTS("KittenML/kitten-tts-nano-0.1")
audio = m.generate("This high quality TTS model which is small in size but works really amazing!")
# Save the audio
import soundfile as sf
sf.write('output.wav', audio, 24000)
Output:
Conclusion
By now, you’ve seen how Kitten TTS combines an ultra-lightweight architecture, CPU-only optimization, and premium-quality voices to deliver real-time, high-fidelity speech synthesis without demanding hardware. Setting it up is straightforward, and with NodeShift Cloud providing a seamless, ready-to-use environment, you can skip complex local configurations and start generating realistic audio in minutes. Together, Kitten TTS and NodeShift Cloud make advanced text-to-speech more accessible than ever, whether you’re building interactive applications, enhancing accessibility, or simply experimenting with voice AI.
