If you've been searching for a powerful, open-source text-to-speech (TTS) model that doesn't compromise on quality or flexibility, Chatterbox might just blow your mind. Developed by Resemble AI, Chatterbox is the first production-grade TTS model that’s not only free to use but also outperforms industry giants like ElevenLabs in direct listening tests. It is built on a 0.5B Llama backbone and trained on an impressive 500,000 hours of cleaned speech data, and this model delivers ultra-stable, high-fidelity speech synthesis with remarkable control. Its standout feature - Emotion exaggeration and intensity control, allowing creators to fine-tune the expressiveness of generated voices, something never before seen in the open-source landscape. If you're designing interactive AI agents, adding emotion to videos and vlogs, or building immersive animated characters, Chatterbox makes your synthetic voices feel real, dynamic, and production-ready. With alignment-informed inference, a handy voice conversion script, and watermarked outputs for traceability, this model is ready for both experimentation and scale.
In this article, we’ll walk you through quick steps to install and run Chatterbox locally, so you can harness its full potential right from your own CPU or GPU accelerated machine.
Prerequisites
The minimum system requirements for this use case are:
GPUs: 1x RTX4090, RTXA6000
Disk Space: 50 GB
RAM: At least 8 GB.
Note: The prerequisites for this are highly variable across use cases. A high-end configuration could be used for a large-scale deployment.
Step-by-step process to install and run Chatterbox
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:
1) Navigate to the menu on the left side.
2) Click on the GPU Nodes option.
3) 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)
1) For this tutorial, we’ll be using the RTX A6000 GPU; however, you can choose any GPU of your choice based on your needs.
2) 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
1) 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 would be to choose an image for the VM, which in our case is Nvidia Cuda, where we’ll deploy and run the inference of our model.
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
1) 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!
2) Once your GPU shows this status, navigate to the three dots on the right and click on Connect with SSH. This will open a pop-up box with the Host details. Copy and paste that in your local terminal to connect to the remote server via SSH.
As you copy the details, follow the below steps to connect to the running GPU VM via SSH:
1) 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
1) Create a virtual environment using Anaconda.
conda create -n chatterbox python=3.11 && conda activate chatterbox
Output:
2) Clone the official repository of ResembleAI/Chatterbox and move inside the project directory.
git clone https://github.com/ResembleAI/chatterbox.git && cd chatterbox
Output:
3) Install chatterbox-tts package.
pip install chatterbox-tts
Output:
4) Install gradio.
pip install gradio
Output:
Step 8: Download the Model
1) Run the Gradio app to load the model.
python gradio_tts_app.py
Output:
After the model is downloaded successfully, the gradio app will go live on local and shareable URLs as shown above. You can either directly access using shareable URL that will be stay accessible for 1 week.
If you instead want to access the app with local URL, you can access it by visiting http://127.0.0.1:7860.
However, if you're on a remote machine (e.g., NodeShift GPU), you'll need to do SSH port forwarding in order to access the local Gradio URL 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>
Once you're connected, you'll be able to access the app on local browser at this URL: http://127.0.0.1:7860
Step 9: Run the Model for Inference
1) Here's how the interface looks like:
2) Finally, we'll test the model with a text and reference audio file.
There's a default text given as shown above, let's replace that with our own text and also upload a reference audio of the speaker that we want the model to clone.
Here's the audio output generated by the model for the above text:
https://drive.google.com/file/d/1FK8hl0I3shhc8b_Y88Qsn4LyDot_LHZP/view?usp=sharing
Conclusion
By now, you’ve explored how to install and run Chatterbox locally, unlocking access to a cutting-edge open-source TTS model with state-of-the-art speech quality, emotional control, and production-ready stability. For creators or developers building complex AI agents, Chatterbox offers unmatched flexibility and realism. And with NodeShift Cloud, setting up Chatterbox becomes even faster and smoother, our optimized infrastructure ensures GPU-accelerated performance, easy deployment, and seamless scaling, so you can focus on building expressive voice experiences without getting bogged down in setup or resource management.
For more information about NodeShift:
Top comments (3)
pretty cool seeing this level of detail for setting stuff up, tbh it always felt like a hassle before - you think guides like this make people less scared to mess with more advanced ai tools?
Thanks! Exactly, That's my end goal as a DevRel to make seemingly advance stuff feel easy.
Some comments may only be visible to logged-in visitors. Sign in to view all comments.