Frank Fu's Blog

Hi, there! Welcome to the second blog of our series on real-time talking digital human! As artificial intelligence technology continues to advance, digital humans are emerging as the next generation of virtual assistants and interactive media, quickly making their way into a wide range of industries and fields, from virtual customer service, online education, intelligent assistants to entertainment. These systems utilize a variety of interaction methods, including text, voice, images, and video. However, despite remarkable advancements in digital human technology, achieving natural and seamless performance in real-time interactions remains a considerable challenge.

This article will delve into an innovative real-time digital human system based on MuseTalk and the OpenAI Realtime API, introducing how this system addresses the shortcomings of traditional technologies and promotes the development of digital human technology towards a more natural and efficient direction.

1. Innovative Advantages of MuseTalk + OpenAI Realtime API

1.1 Challenges in Traditional Digital Human Technologies

Traditional digital human technologies often face several critical challenges:

（1）Latency Issues: Real-time audio and video processing often suffers from high latency due to multiple stages, including speech recognition, text generation, audio synchronization, and video rendering. This delay disrupts the interaction flow, resulting in a less smooth and engaging user experience.

（2）Difficulty in Lip Synchronization: Despite the availability of various facial animation and lip synchronization technologies, achieving precise alignment in real-world applications remains a significant challenge. Audio and lip movements frequently fall out of sync, creating an unnatural and less immersive experience for users.

（3）Low Interaction Quality: Most traditional digital human technologies rely on preset models, making it difficult to handle complex natural language interactions, which can result in mechanized and monotonous interactions.

1.2 Innovative Solutions for Seamless Interactions of Real–time Digital Human

For innovative real-time digital human, the solution powered by MuseTalk and the OpenAI Realtime API addresses these bottlenecks, delivering a more natural and seamless real-time interaction experience. Key advantages include:

（1）Higher Real-Time Responsiveness and Low Latency: The combination of MuseTalk and the OpenAI Realtime API allows for real-time processing of audio input and synchronous generation of high-quality lip synchronization animations.

（2）Smooth Lip Synchronization and Natural Expression: MuseTalk employs Latent Space Inpainting technology to generate precise lip synchronization animations, eliminating the stuttering and lack of fluidity issues found in traditional methods.

（3）Cross-Modal Interaction Capabilities: OpenAI’s powerful natural language processing capabilities enable digital humans to understand complex dialogue, making their performance more diverse.

（4）Lower Cost and Hardware Requirements: The integration of MuseTalk and the OpenAI Realtime API significantly reduces reliance on high-end hardware. Basic audio input and fundamental facial animation generation technologies suffice to achieve high-quality real-time lip synchronization and voice output.

（5）High Precision in Speech and Context Understanding: The OpenAI Realtime API, based on advanced pre-trained models, can comprehend complex contexts and multi-turn dialogues.

（6）Greater Adaptability and Flexibility: The flexibility of the OpenAI Realtime API allows for quick adaptation to different languages, emotional expressions, and scenario requirements.

（7）Support for Large-Scale Real-Time Interaction Scenarios: Leveraging the low latency and efficient responsiveness of the OpenAI Realtime API, our system can manage large-scale real-time interactions.

（8）Scalability and Continuous Optimization: With the enhanced scalability provided by MuseTalk and the OpenAI Realtime API, we can quickly integrate new features. These include speech emotion analysis and personalized dialogue management. At the same time, we can implement continuous optimizations to improve performance and deliver a better user experience.

2. System Architecture and Design of Real-time Digital Human

To realize these advantages, we have designed a flexible, efficient, and low-latency system architecture to ensure that our real-time digital human seamlessly integrates audio, video, and natural language processing.

2.1 System Overview

The real-time digital human system based on MuseTalk and the OpenAI Realtime API is designed with efficient audio-video synchronization at its core, supporting low-latency real-time interaction and natural fluid facial animations. The system consists of the following modules:

（1）Frontend Digital Human Interaction Interface: Serves as the entry point for user-system interaction, triggering audio processing and animation generation through user click actions.

（2）WebSocket Communication Module: Utilizes Java and JavaScript WebSocket connections to handle audio transmission and dialogue generation, ensuring the real-time nature of streaming data.

（3）MuseTalk Audio-Video Generation Module: Receives audio data from the OpenAI Realtime API and transforms it into lip-synchronized animation videos.

（4）Audio and Video File Management: The system monitors folders for audio input and video output, processing and cleaning audio-video files in real-time to avoid resource accumulation.

2.2 System Architecture Design

The diagram below illustrates the complete architecture design of the system, detailing user interaction, data flow, and module functionality.

（1）User Initialization:

The user starts by clicking the “Start” button on the frontend interface, which triggers audio recording, WebSocket communication, and backend processing logic.

Once the system starts, it creates necessary audio and video folders, initializes MuseTalk’s processing threads, and sets up the video result reading threads.

The frontend interface continuously plays a silent MP4 video of the digital human in a loop.

（2）Frontend to Backend Communication:

JS WebSocket: Responsible for listening to messages returned by OpenAI Realtime API (excluding the response.audio.delta audio stream event). It processes the received event types to handle video stream playback, interrupt video playback, and other user interactions.

Java WebSocket: This component processes audio stream data from the OpenAI Realtime API, storing it in the designated audio folder. Subsequently, it retrieves the processed video files from the video folder and streams them back to the frontend. Through these steps, it ensures smooth data flow and reliable interaction.

（3）Audio and Lip-Sync Synchronization:

MuseTalk Processing Thread:

Continuously reads the audio folder contents.

Processes the audio data returned by the OpenAI Realtime API via MuseTalk to drive facial animations and generate lip-synced videos.

Deletes processed audio files after video generation.

Video Result Handling:

Videos are stored in the designated video folder in real-time.

The video result reading thread streams the generated videos back to the frontend for playback.

Deletes processed video files after streaming.

（4）User Interruption:

If the user interrupts the digital human’s video stream playback, an input_audio_buffer.speech_started event will be returned by the OpenAI Realtime API. At this point:

The video stream playback is stopped.

The corresponding audio and video folders are cleared.

（5）User Termination:

When the user clicks the “Stop” button:

The system terminates the WebSocket connection.

All temporary files are cleaned up.

MuseTalk processing threads and video result reading threads are closed.

The frontend interface resumes looping the silent MP4 video of the digital human.

Key Technical Points

Real-Time Streaming Processing

（6）OpenAI Realtime API:

It supports streaming audio input and delivers results in real time. Moreover, by adopting a streaming data processing approach, it significantly reduces latency and enhances responsiveness.

WebSocket Bidirectional Communication:

Data format design ensures the independent flow of audio and video data streams.

Event-driven mechanisms improve interaction efficiency.

MuseTalk Audio-Video Synchronization

（7）Source Code Modifications:

The original project uses command-based input，for example, one non-speaking character video + multiple audio files) , to generate multiple lip-synced videos.

The source code was updated to process audio in real time, eliminating the need for command-line executions. This change reduces additional time overhead and improves overall efficiency.

（8）Parameter Tuning:

Runtime parameters require frequent adjustments, such as fine-tuning the bbox_shift value to optimize synchronization between audio and lip movements.

Apart form this, it is crucial to determine the optimal length of audio (in seconds) to process at a time. This ensures that the video generation speed exceeds the playback speed on the frontend, maintaining a steady and consistent output-consumption flow.

（9）Multi-Threaded Processing

Avoiding Thread Blocking: Each task module runs on independent threads to avoid mutual blocking:

Audio monitoring thread

Video generation thread

Video playback thread

File Management Optimization

Dynamic Folder Monitoring:

Real-time monitoring of new content in folders to automatically trigger audio processing and video generation.

Cleaning mechanisms ensure that only the currently processed content remains in the folders.

Efficient Resource Management:

Prevent file accumulation, which could lead to disk space issues or processing delays.

3. Preliminary Implementation

This section is all about implementation details and code. Since it’s a bit too lengthy to include here, I’ve put it into the following document that you can download and check out!

Thank you all for reading the second blog of our series on real-time talking digital human. Stay tuned for the next blog, where I’ll explore how MuseTalk and the OpenAI Realtime API enable seamless real-time digital human interactions, along with the technical challenges, solutions, architecture design, and actual runtime samples from the project.

Additionally, a video version of this blog is available below！Welcome to explore my Youtube channel https://www.youtube.com/@frankfu007 for more exciting content. If you enjoy my video, don’t forget to like and subscribe for more insights!

Interested in more application of Realtime API in practical projects? Explore my articles linked below!

• Realtime API Model Comparison
• OpenAI RealtimeAPI+MuseTalk: Technical Challenges and Solutions 3
• OpenAI RealtimeAPI+MuseTalk: Make a Realtime Talking Digital Human Facial Animation and Lip Syncing 1
• How to Build a Multi-Agent System Integrated with OpenAI Realtime API