SeedRealtime – ByteDance's Native Audio-Video Full-Duplex Large Model

1. What is SeedRealtime

SeedRealtime is a native audio-video full-duplex large model introduced by ByteDance's Seed team. It integrates audio, video, and text within a unified architecture, enabling real-time, full-modal interaction where users can see, hear, and speak simultaneously. This model features three core breakthroughs: joint audio-video understanding, proactive interaction, and smooth conversational rhythm. End-to-end evaluations show that conversational rhythm issues are reduced by half compared to cascaded models. It has now been fully launched on the Dou+ App, becoming the first large-scale deployed audio-video full-duplex AI product in the industry.

Image source: Official article
Image source: official article

Technical positioning and domain: SeedRealtime belongs to the field of multimodal large models, focusing on real-time audio-video full-duplex interaction. Unlike traditional cascaded systems (ASR → VLM → TTS), it uses a single model to natively fuse three modalities—audio, video, and text—processing perception, understanding, decision-making, and expression simultaneously within the same architecture, avoiding information loss and latency accumulation caused by multi-stage sequential processing. The model is positioned as an end-to-end full-duplex interactive agent, suitable for scenarios requiring real-time visual and auditory collaboration.

Development background: This model was developed by ByteDance's Seed team, which has deep technical expertise in the field of AI large models and has previously launched multiple achievements in the Seed series. The motivation for development stemmed from existing cascaded systems' issues in real-time interaction, including response latency, rigid conversational pacing, and fragmented multimodal information. SeedRealtime aims to achieve human-like natural conversational rhythm through a unified architecture, allowing AI to see, hear, and speak simultaneously, while perceiving environmental changes and proactively engaging in interaction.

Core value: SeedRealtime addresses three major pain points in traditional cascaded systems: first, understanding deviations caused by multi-stage information loss, especially in scenarios such as homonym disambiguation and temporal reference; second, rigid turn-taking caused by external VAD (Voice Activity Detection) rules, which prevent natural "simultaneous speaking and listening"; third, the lack of proactive environmental perception, resulting in only passive responses. Through its native full-duplex architecture, the model can autonomously determine conversation timing, distinguish target speech in noisy environments, and proactively alert users when the visual state changes, upgrading interaction from passive Q&A to active collaboration.

Technical features: Key technical features include an end-to-end unified audio-video modeling framework that integrates perception, understanding, decision-making, and expression within the same model; native full-duplex interaction that does not rely on external VAD rules, allowing the model to continuously make conversational state decisions based on multimodal information streams; audio-video temporal alignment, combining scene, gesture, gaze, and historical actions to understand user intent; and engineering optimization for low latency, compressing end-to-end delay through chunked input and streaming generation. These features enable SeedRealtime to significantly outperform cascaded solutions in conversational rhythm, noise resistance, and cross-modal understanding.

2. Key Features

• Multimodal Understanding of Audio and Video: Deeply integrates sound, visuals, and temporal information natively. It can resolve ambiguities of homonyms by combining visual context (e.g., clarifying what "this" refers to in the visual scene), and accurately understand temporal references (e.g., "that action just now"), achieving a unified perception of what is seen, heard, and spoken. This capability is especially crucial in Chinese scenarios, effectively handling common ambiguities and homonym confusion in spoken language.

• Proactive Interaction Ability: Possesses continuous environmental perception and proactive expression capabilities, allowing it to actively alert users when visual states change (e.g., "The product you're interested in is now available") and invoke tools within its responses (e.g., checking the weather, setting reminders), transforming passive responses into proactive collaboration. This turns the model from a simple Q&A tool into a proactive intelligent assistant.

• Smooth Interaction Rhythm: Real-time perception of user dialogue status and interaction rhythm enables the model to naturally interject, pause, and respond at appropriate moments. It has strong noise resistance, capable of distinguishing between background conversations and ambient noise. End-to-end evaluations show that the model reduces dialogue rhythm issues by half compared to cascaded models, offering an interaction experience closer to human conversation.

• Multi-person Scene Recognition: Simultaneously identifies faces, distinguishes sound sources, and understands content in multi-person and noisy environments, continuously mapping each voice to its corresponding identity. For example, in a home setting, the model can differentiate between questions from parents and children and respond accordingly, avoiding confusion.

• Engineering Low-latency Optimization: Through segmented audio and video input and streaming generation, combined with efficient quantization and inference optimization, the model continuously compresses the end-to-end latency of "hearing—understanding—responding." This optimization enables real-time interaction, making the waiting time almost imperceptible to users.

• Cross-modal Coreference Resolution: Can resolve cross-modal references such as "this" or "there" by combining visual context. For instance, if a user points to an object in the visual scene and says, "How much is this?" the model automatically associates the visual target and provides a response. This feature significantly enhances the naturalness and efficiency of interaction.

3. How to Use

1. Environment Requirements: To use SeedRealtime, you need a smartphone running iOS or Android with the latest version of the DouPao App installed. The device must be equipped with a camera and microphone, and have a stable network connection. This feature is now fully available, and no additional application or configuration is required.

2. Update the DouPao App: Go to the official app store (Apple App Store or Android app market) and update the DouPao App to the latest version. Older versions may not include the SeedRealtime feature, so please make sure to confirm the version number.

3. Enter Voice Call: After opening the DouPao App, click the "Call" button within any chat interface to enter the voice call interface. At this point, the model operates in audio mode, supporting real-time voice interaction.

4. Switch to Video Mode: Once in the call interface, enable camera and microphone permissions and click the button to switch to video call. The model will automatically activate full-duplex audio and video mode, beginning to synchronize the perception of visuals and sound.

5. Start Real-Time Interaction: While displaying the visuals, speak naturally. The model will simultaneously perceive the audio and video streams and respond in real time. Users do not need to wait for the previous sentence to finish before continuing to speak; the model will autonomously determine the conversation timing and naturally respond.

6. Use Proactive Observation: You can issue continuous observation instructions (e.g., "Help me watch this screen and remind me when a red button appears"). The model will proactively alert the user when the target appears or the screen changes. This feature is especially useful in scenarios requiring long-term monitoring.

Notes: When using video mode, ensure that the lighting is sufficient to avoid overly dark visuals that may affect visual recognition. In noisy environments, it is recommended to speak close to the microphone to ensure clear audio. The proactive observation feature requires a clear description of the observation target; the model will continuously analyze the video stream based on the instruction.

4. Pros and Cons Analysis

5. Comparative Analysis with Similar Tools

Selection Recommendations: For applications requiring real-time full-duplex audio-video interaction and primarily operating in Chinese scenarios (such as intelligent customer service, home education, and on-site tour guiding), SeedRealtime is the optimal choice. Its end-to-end unified architecture and proactive environmental perception capabilities are unique among similar products, and it has already been scaled through integration with the Douyin App, with proven user experience. If the application scenario requires multilingual support or deep integration with the Google ecosystem (such as office automation and Android system-level interaction), Gemini Live is a more suitable option, with its strong multimodal capabilities and latency performance.

For developers requiring flexible custom deployment and open-source control, Moshi provides full-duplex audio capabilities and can be run locally, but its lack of visual support is a drawback. While GPT-4o has strong multimodal capabilities, it has limitations in full-duplex and real-time video stream processing, making it more suitable for non-real-time multimodal analysis tasks. Overall, SeedRealtime has a clear first-mover advantage and differentiated capabilities in the niche area of full-duplex audio-video interaction, especially suitable for scenarios in Chinese environments that prioritize natural interaction experiences.

6. Editor's Summary

SeedRealtime demonstrates notable forward-thinking in technological innovation. Its end-to-end unified audio-visual modeling framework breaks free from the architectural constraints of traditional cascaded systems, integrating perception, understanding, decision-making, and expression within a single model that operates synchronously. This design fundamentally reduces information loss and latency accumulation across multiple stages. Native full-duplex interaction does not rely on external VAD rules, allowing the model to autonomously determine conversation timing like humans do, maintaining a smooth communication rhythm even in multi-person and noisy environments. Audio-visual temporal alignment technology, combined with scene, gesture, gaze, and historical action understanding, enables the model to infer user intent, achieving cross-modal disambiguation and reference resolution. This is particularly important in Chinese spoken interaction, effectively addressing challenges such as homonyms and ambiguous references. Engineering optimizations for low latency, through chunked input and streaming generation, compress end-to-end latency to an acceptable real-time level, laying the foundation for large-scale deployment.

In terms of practical value, SeedRealtime has been fully launched on the Douyin App, becoming the first industry-wide deployed full-duplex AI product for audio and video. This means its technical maturity has been validated by a large user base. In scenarios such as intelligent tour guides, foreign language practice partners, device operation guidance, and travel information assistants, this model significantly enhances interaction efficiency and naturalness. In particular, the active environmental perception feature upgrades AI from a passive Q&A tool to an active collaborative partner, a shift with significant product implications. For applications requiring real-time visual and auditory coordination, SeedRealtime provides the most realistic human-like interactive experience available today.

In terms of target users, SeedRealtime primarily serves general consumers through the Douyin App, while also offering technical references and industry benchmarks for developers. Although enterprises and developers currently cannot deploy it independently, its technical approach and performance validation point the way for the development of full-duplex multimodal interaction. In the future, if ByteDance opens up its API or provides on-device deployment options, SeedRealtime is likely to realize greater value in fields such as smart home devices, in-vehicle interaction, and robotics. Overall, SeedRealtime has set a new technical benchmark in the field of full-duplex audio-visual interaction, and its unified architecture and proactive interaction philosophy will drive multimodal AI toward a shift from "question-and-answer" models to "collaborative" models.

7. Application Scenarios

• Smart Tour Guide: While visiting a museum or tourist attraction, users can point their camera at an exhibit, and the model continuously observes the scene. When the target exhibit appears, the model automatically provides historical background and craftsmanship details. Users can ask questions like "What does this pattern signify" at any time, and the model will provide real-time answers by combining visual and speech understanding, enriching and personalizing the visit experience.

• Foreign Language Practice Partner: Users can practice foreign language conversations with the model, which provides real-time pronunciation correction and example sentence construction based on the visual context. In noisy environments, the model remains focused on the target learner, distinguishing between casual conversation and formal questions, ensuring uninterrupted practice. For example, if a user points to a word in a book and asks, "How do you pronounce this word," the model will immediately provide the correct pronunciation and demonstrate its usage.

• Device Operation Guidance: When operating complex equipment (such as instruments or household appliances), users can point their camera at the device's panel, and the model will provide real-time error correction and adjustment suggestions based on visual state changes. For instance, when using an oven, if the model detects that the knob is in the wrong position, it will proactively alert the user, "The temperature is set too high. Please adjust it to 180 degrees," preventing operational mistakes.

• Travel Information Assistant: In noisy environments such as airports or train stations, users can point their camera at large screens displaying flight information, and the model will identify the screen content and answer questions about arrival times, boarding gates, etc. Users can directly ask, "Which boarding gate is my flight at," and the model will provide accurate guidance by combining real-time visual and speech input, eliminating the need for manual searches.

• Children's Educational Tutor: When accompanying children in their studies, users can turn on the camera to observe the content in real time. The model corrects pronunciation during reading sessions and provides step-by-step guidance when solving math problems. Even with other family members speaking in the background, the model remains focused on the target child's speech and visual input, offering a stable and consistent tutoring experience.

8. FAQ

Q: What hardware is required to use SeedRealtime?
A: Currently, SeedRealtime operates through the DouPao App, requiring a smartphone with either iOS or Android operating system, equipped with a camera and microphone. The cloud processing is handled by ByteDance servers, while the smartphone is only responsible for audio and video capture and result display. Therefore, the phone's performance requirements are not high, and mainstream smartphones can use it smoothly.

Q: Is SeedRealtime free to use?
A: As of May 2025, SeedRealtime features are fully available in the DouPao App, and users can use the audio and video calling functions without additional charges. However, note that the network traffic generated during use is the user's responsibility. Whether paid premium services will be introduced in the future should be confirmed through official announcements.

Q: How can I update the DouPao App to use SeedRealtime?
A: Please go to your phone's official app store (Apple App Store or Android app market), search for "DouPao," and update to the latest version. Older versions may not include this feature. It is recommended to restart the app after updating and ensure that camera and microphone permissions are enabled.

Q: What languages does SeedRealtime support?
A: Currently, SeedRealtime is primarily optimized for Chinese scenarios, excelling in areas such as Chinese homonym disambiguation and spoken language reference resolution. The official has not explicitly stated the level of support for English and other languages, but based on its multimodal architecture, it is theoretically capable of handling multilingual input. Actual performance requires further testing.

Q: How is privacy and security ensured with SeedRealtime?
A: During use, the camera and microphone continuously collect audio and video data, which is sent to the cloud for real-time processing. ByteDance states that it will follow relevant privacy policies and data security standards. However, specific data storage, encryption, and deletion strategies are recommended to be reviewed by users in the DouPao App's privacy policy. For sensitive scenarios, users can choose to disable the video mode and use only audio calls.

Q: What is the difference between SeedRealtime and regular video calls?
A: Regular video calls only transmit audio and video signals, while SeedRealtime can understand the content of the audio and video and respond in real time. It can not only "see" objects, text, and actions in the video, but also "understand" the user's natural language, enabling intelligent interaction based on context. For example, if a user points to an item in the video and asks, "How much does this cost?" the model will automatically identify the item and provide a response—an ability traditional video calls cannot achieve.

Q: How does SeedRealtime perform in noisy environments?
A: SeedRealtime has strong anti-interference capabilities and can accurately distinguish background chatter from formal questions. In multi-person scenarios, it can also synchronize face recognition and sound source identification, matching each person's voice with their identity. Actual testing shows that it reduces issues with conversation pacing and false triggers by half compared to cascaded models, making it suitable for use in real-world complex environments such as cafes and homes.

9. Project Links

• Project Website: https://seed.bytedance.com/en/seedrealtime