Traditional voice AI pipelines typically involve speech-to-text, LLMs for inference, and text-to-speech, but real-time video generation is significantly more complex.
Tavus, initially an AI research company, developed an end-to-end conversational video interface to enable real-time interaction with replicas of individuals.
The Tavus system aims for a response time of around 600 milliseconds, though this sometimes needs to be adjusted.
Tavus utilizes proprietary models like Sparrow Zero and Raven Zero, which will be offered for integration into frameworks like Pipecat.
Pipecat is an open-source, vendor-neutral orchestration framework designed for real-time AI, providing observability and control over conversational flows.
It addresses infrastructure challenges in production AI applications, such as understanding bot behavior, capturing metrics, and diagnosing response delays.
Pipecat handles three core functions: input (receiving user media like audio/video), processing (running various models and integrating user video for responses), and output (delivering synchronized video and audio).
The framework is built on three fundamental pieces: frames (data containers for media snippets or events), processors (which transform frames), and pipelines (which define the asynchronous, low-latency operation of the bot).
A typical Pipecat pipeline involves frames flowing from transport input to a speech-to-text processor, then a context aggregator, an LLM processor (streaming text frames), and finally text-to-speech and video generation (e.g., via Tavus) before transport output.
Pipecat's flexibility allows for advanced use cases, such as parallel pipelines for real-time sentiment analysis or detecting whether a call is answered by a human or voicemail.
Tavus and Pipecat Partnership & Future Models 14:03
Tavus is integrating its advanced models into Pipecat, recognizing that Pipecat's orchestration capabilities can save months of development time.
Upcoming Tavus models include a multilingual turn detection model that determines when a person has finished speaking, crucial for faster AI responses without interruptions.
A response timing model will soon be integrated into Pipecat, allowing the AI to adjust its response speed based on the conversational context (e.g., slowing down for sensitive topics).
A multimodal perception model will analyze user emotions and surroundings, feeding this data into the turn-taking and response timing models for more nuanced conversational flow.
Deploying real-time AI bots requires a REST API to initiate conversations and quickly spin up bot instances, along with a transport layer (like WebRTC) for media transfer.
Pipecat Cloud is available for users who want to deploy bots at scale without managing complex infrastructure like Kubernetes.