Frank Fu's Blog

Hello everyone, and welcome to the third blog in our series on real-time digital human! In this article, we’ll dive into how MuseTalk and the OpenAI Realtime API work together to enable seamless real-time digital human interaction. We’ll also discuss the technical challenges we faced during development and the solutions we implemented to overcome them. Additionally, I’ll showcase some actual runtime sample outputs. This blog will also cover the project’s architecture design, technology choices, and other key details.

1. Project Goal Overview: Building an Efficient Digital Human Interaction System

Before we begin, let’s briefly review the project goals. Our aim is to create an efficient and accurate real-time digital human interaction system. Further more, for a smooth user experience, this system should synchronize lip movements and audio with low latency, ensuring a perfect integration of video and audio. Our core objective is to use MuseTalk for generating real-time lip-sync animations, while the OpenAI Realtime API produces natural, fluid speech. Together, they ensure perfect synchronization between audio and visual elements.

Although this goal may seem straightforward, we encountered several complex technical challenges during the actual development process.

2. Effect Samples

We will directly run the audio blocks returned by the Realtime API and observe the output video effects. Detailed demonstrations can be found in the YouTube video.

3. Technical Challenges and Solutions: Enhancing Digital Human Interaction

3.1 Audio Block Handling and Lip Sync Synchronization

To synchronize audio and video, our initial solution was to use the audio blocks from the OpenAI Realtime API. These blocks directly drove the generation of lip-sync animations for the video. Initially, this approach seemed effective, but as the project progressed, issues gradually emerged.

Problem Analysis: If the audio blocks are too short, the video’s lip movements often fall behind the audio rhythm. This leads to unnatural video output. Specifically, the short duration of audio blocks is overly precise, failing to capture changes in lip movements effectively. This issue is especially noticeable during fast speech or emotional scenes, where lip movements appear stiff and jerky.

Solution: To address the above problem, we decided to standardize the duration of the audio blocks to approximately 2 seconds. This adjustment optimized video synchronization and improved the transition between audio and video. Additionally, we fine-tuned the timing coordination between the audio blocks and video, making the audio rhythm more consistent and enhancing overall smoothness.

3.2 Latency Issues: Key to Smooth Digital Human Interaction

Another major technical challenge for real-time digital human interaction was system latency, particularly in high concurrency and complex request scenarios. The delay in processing audio blocks and generating videos was quite noticeable, negatively impacting the overall user experience.

Problem Analysis: The root cause of the latency was the synchronization issue in multithreaded processing. After receiving the audio blocks, we needed to process them in real-time and generate the video. Any delay in this process directly affected the system’s response speed.

Solution: We optimized the audio block duration and set video generation to run slightly faster than the playback speed. By adjusting the thread priority for video rendering, we could generate video content ahead of time during audio playback. This reduced latency and improved the system’s responsiveness and smoothness.

Additionally, we employed asynchronous processing and parallel computation, allowing audio and video processing to occur simultaneously across multiple threads. This reduced the performance impact of single-threaded processing and significantly increased the overall speed and concurrency of the system.

3.3 I/O Operation Bottlenecks

In addition to audio-video synchronization and latency, performance bottlenecks also arose from I/O operations. Initially, audio data was stored in an audio folder, and the generated videos were saved in a separate video folder. While this approach ensured data persistence, frequent file read/write operations greatly increased system latency. This was especially problematic in multithreaded environments, where resource contention became more apparent.

Problem Analysis: Frequent file read/write operations heavily loaded the system, slowing data transfer speeds and disrupting audio-video synchronization, especially under heavy load.

Solution: To address this, we stored both audio and video data in memory, eliminating the delay caused by file I/O operations. Audio data was passed directly to the video processing module through binary streams, and the generated video was transmitted to the frontend in the same way, bypassing file storage. This reduced I/O operations and boosted system response speed.

Additionally, we implemented memory caching to further optimize data transfer, ensuring efficient, real-time processing and improving overall performance.

3.4 Resource Optimization and GPU Acceleration

As the project progressed, we realized that software optimizations alone were insufficient, especially when high-performance video rendering was required. The need for hardware acceleration became increasingly critical.

Problem Analysis: During real-time video generation, it is quiet clear that the CPU’s computational power was inadequate for high-load scenarios. Specifically, when handling large-scale image rendering, the CPU faced excessive computational pressure, leading to performance bottlenecks.

Solution: After research and testing, we chose GPU acceleration to enhance video generation efficiency. We used the NVIDIA Tesla V100 GPU, known for its advantages in video rendering and audio generation. Its parallel computing capabilities significantly improved processing efficiency and were recommended by MuseTalk. This approach boosted video generation performance and allowed the system to handle more concurrent requests.

Moreover, GPU acceleration not only sped up video rendering but also effectively managed the system’s resource consumption when handling complex images and high-quality video.

4. Conclusion and Future Outlook

After multiple rounds of optimization, we successfully overcame numerous technical challenges, significantly enhancing the system’s stability and performance. Our real-time digital human interaction system has achieved noticeable progress in audio-video synchronization, meeting the desired goals for latency and performance.

However, technological advancement is an ongoing journey. As demands grow and technology evolves, we will continue to optimize the system, focusing on areas like hardware acceleration and multithreaded processing. These improvements aim to further enhance processing capabilities and response speed, enabling the system to address more complex application needs effectively.

Thanks for follow us with the real-time digital human series! Additionally, a video version of this blog is available below! If you’re interested in the implementation details of MuseTalk or OpenAI Realtime API, or have any questions, feel free to leave a comment.

And 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：Make a Realtime Talking Digital Human Facial Animation and Lip Syncing 2
• 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