GLM-4-Voice
GLM-4-Voice offers innovative AI tools for creating immersive voice-driven web experiences effortlessly and efficiently.
English
English
What is GLM-4-Voice?
GLM-4-Voice is an advanced end-to-end voice model developed by Tsinghua University. It can understand and generate both Chinese and English speech, enabling real-time voice conversations. This model uses cutting-edge speech recognition and synthesis technologies to achieve seamless conversion from speech to text and back to speech. It offers low latency and high-intelligence dialogue capabilities, optimized for emotional expression and speech synthesis.
Target Users:
The target audience for GLM-4-Voice includes developers, businesses, and individuals or organizations that require real-time voice interaction. For developers, it provides a powerful tool to build voice interaction applications. For businesses, it can enhance customer service efficiency and quality. For individual users, it offers a new form of voice interaction experience.
Example Scenarios:
Use a gentle voice to guide users into relaxation.
Use an excited voice to commentate on a soccer match.
Use a mournful voice to tell a ghost story.
Key Features:
Speech Recognition: Converts continuous speech input into discrete tokens.
Speech Synthesis: Transforms discrete speech tokens into continuous speech output.
Emotional Control: Adjusts voice emotions, intonation, speed, and dialect based on user instructions.
Streaming Inference: Supports alternating stream output of text and speech, reducing end-to-end conversation delay.
Pre-training Capabilities: Trained on millions of hours of audio and billions of text tokens, with strong audio understanding and modeling capabilities.
Multilingual Support: Can directly understand and generate Chinese and English speech for real-time conversations.
Getting Started Guide:
1. Clone the repository using Git commands.
2. Install Python dependencies using requirements.txt.
3. Download the required voice model and tokenizer according to project guidelines.
4. Start the model service by running model_server.py.
5. Launch the Web Demo by running web_demo.py.
6. Access the Web Demo at http://127.0.0.1:8888 in your browser.
