GLM-4 Voice: Talk to AI in Realtime using Voice! (Open source)

Introduction

In the world of artificial intelligence, an exciting development has emerged: the GLM-4 Voice model. This open-source end-to-end speech large language model allows users to engage in natural language conversations with AI, responding in real time. The unique architecture of the model facilitates speech-to-speech interactions, enabling seamless communication.

Model Architecture

When you speak to the GLM-4 Voice model, it utilizes a speech tokenizer to convert your spoken words into an input format understood by the model. The model then generates responses in both text and speech formats. This process includes decoding the speech using a speech decoder. Consequently, users can receive information, answers to questions, or simply enjoy entertaining conversations, all powered by voice interaction.

Key Features

The GLM-4 Voice offers several impressive features:

Integrated System: It combines speech recognition, language understanding, and speech generation into one cohesive model.
Bilingual Support: The model supports both Chinese and English, catering to a diverse user base.
Emotion and Tone Adjustment: Users can modify the emotional tone and style of responses in real time.
Real-time Interaction: Quick response times enhance user experience and improve human-machine interaction.

Applications

The GLM-4 Voice model is applicable in various fields including:

Customer service
Entertainment
Education

As one of the trending models on Hugging Face, it has rapidly gained popularity, totaling over a thousand stars shortly after its release.

Getting Started Locally

Running the GLM-4 Voice model on your own computer is simple. Below are step-by-step instructions for setting it up:

Install Prerequisites: Ensure you have GPU capabilities (an example is the RTX A6000) and a virtual CPU.
Clone the Repository: Use Git to clone the GLM-4 Voice repository with the command:
```
git clone --recurse-submodules [URL]
```
Navigate to the Folder: Change your directory to the cloned GLM-4 Voice folder.
Install Dependencies: Install the necessary packages with:
```
pip install accelerate
pip install -r requirements.txt
```
Install Git LFS: Install Git LFS with:
```
apt install git-lfs
```
Clone the Hugging Face Decoder: Clone the repository for the GLM-4 Voice decoder:
```
git clone [Hugging Face URL]
```

Running the Model

After setting everything up, it's time to initiate both the backend and frontend:

Start the Backend: In your terminal, run:
```
python model_server.py [full path]
```
This will download the necessary models and start the backend server, which will run on a specified URL.
Start the Frontend: Open another terminal and execute:
```
python web_demo.py [full path]
```

Upon loading, the frontend will showcase various input options and debug information.

Testing the Features

Once the application is running, you can begin testing its capabilities. Users can ask the model to provide information or advice, and it will generate responses in both audio and text formats in real time. For instance, one might ask for a plan for the day and receive a structured response detailing meal suggestions.

Conclusion

In summary, the GLM-4 Voice is a powerful tool for real-time voice interactions with AI. It not only shows promise in enhancing communication with technology but also provides a foundation for future applications in diverse fields.

Keywords

GLM-4 Voice
Speech recognition
Natural language processing
Real-time interaction
Open-source AI
Hugging Face
Customer service
Bilingual support
Emotion adjustment

FAQ

What is GLM-4 Voice?
GLM-4 Voice is an open-source speech large language model that facilitates real-time voice interaction with AI, allowing for seamless conversations in natural language.

How does GLM-4 Voice work?
The model works by converting spoken input into text using a speech tokenizer, generating a response in text and speech, and then decoding it for output.

What platforms or languages does GLM-4 Voice support?
GLM-4 Voice supports both English and Chinese, making it accessible to a wider range of users.

Can I run GLM-4 Voice on my computer?
Yes, you can run GLM-4 Voice locally by following the setup process involving cloning the repository and installing dependencies on a machine with GPU capabilities.

What applications can GLM-4 Voice be used for?
The model can be applied in various fields such as customer service, entertainment, and education, enhancing interaction experiences.