Creating AI companions that feel genuinely interactive requires more than just clear, high-quality speech. It demands real-time adaptability and deep contextual awareness. Traditional text-to-speech (TTS) models generate spoken output directly from text but lack the ability to adjust based on conversational nuances like tone, rhythm, and history. This leads to the well-known one-to-many problem: a sentence can be spoken in countless ways, but only a few match the intended context. Most speech models fail to capture this depth, making AI conversations sound robotic or disconnected. Sesame CSM 1B changes the game by introducing a Conversational Speech Model that treats speech generation as an end-to-end multimodal learning task. Unlike conventional two-stage methods that separate semantic and acoustic processing, Sesame CSM 1B integrates both seamlessly, ensuring expressive, natural speech while maintaining real-time efficiency.
If your aim is to develope AI-powered assistants or virtual content creators, this model enhances conversational AI with unparalleled fluency and contextual precision. Installing Sesame CSM 1B is the first step toward unlocking the features of this model, let’s dive into the step-by-step installation process.
Prerequisites
The minimum system requirements for this use case are:
- GPUs: RTX 4090 or RTX A6000 (for smooth execution).
- Disk Space: 200 GB
- RAM: At least 16 GB.
- Jupyter Notebook installed.
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 & run Sesame CSM 1B
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 the RTX 4090 GPU; however, you can choose any GPU of your choice based on your needs.
- 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 VMs in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x RTX 4090 GPU node with 12 vCPUs/96GB RAM/200 GB 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 Jupyter Notebook, 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
- 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 and click on Connect with SSH. This will open a new tab with a Jupyter Notebook session in which we can run our model.
Step 7: Setting up Jupyter
Start by opening a Terminal inside the Jupyter Lab.
Next, If you want to check the GPU details, run the following command in the terminal:
nvidia-smi
Step 8: Clone Official Repository and Install dependencies
- Once you have installed Anaconda set up in your system, start by creating a virtual environment with
conda.
conda create -n csm python=3.10 -y && conda activate csm
Output:
2. Clone official model repository.
git clone https://github.com/SesameAILabs/csm.git
Output:
3. Move inside the model directory and install dependencies.
cd csm
pip install -r requirements.txt
Output:
Step 9: Download Model files and run the model
Since, this is a gated model, we’ll need to login with Hugging Face access token in order to download the model.
- Login to Hugging Face through CLI.
huggingface-cli login
Enter your Hugging Face READ Token when prompted.
Output:
2. Finally, we’ll run and test the model by running the run_csm.py.
This file already has a conversation input by default. For this tutorial, we’ll use the same conversation template to generate the conversational speech audio. However, you can test with different conversation templates by replacing the below mentioned portion with your own template to generate audios as per your requirements.
Run the following command to start downloading the model checkpoints and generate speech:
python run_csm.py
Output:
You’ll see a success message like above, once the generation is complete.
Finally, you can find the full conversation audio inside the project directory as shown below:
Here’s the link to the generated audio:
https://drive.google.com/file/d/1CVsFju1mT65IsE-MPTiIAFJCcNlWapw8/view?usp=sharing
Conclusion
Sesame CSM 1B revolutionizes AI-driven speech by addressing the one-to-many problem with an end-to-end multimodal learning approach, enabling real-time, context-aware speech generation. Deploying this advanced model requires a robust infrastructure, and that’s where NodeShift’s cloud platform comes in. With its optimized and affordable compute environments and seamless scalability, NodeShift simplifies model deployment, ensuring high performance and minimal latency. If you’re experimenting with AI speech synthesis or scaling production workloads, NodeShift provides the reliability and efficiency needed to bring next-gen conversational AI to life.
