The article also provides a basic example of a Pipecat bot that greets a user when they join a real-time session, using Daily for real-time media transport and ElevenLabs for text-to-speech. For production use, it recommends using WebRTC for client-server audio and suggests using Daily for quick setup. The article also explains the importance of Voice Activity Detection (VAD) for detecting when a user has finished speaking to the bot. Lastly, it provides instructions for setting up a virtual environment for hacking on the framework and configuring your editor for PEP 8 formatting.
Key takeaways:
- Pipecat is a framework for building voice and multimodal conversational agents, with applications ranging from personal coaches to customer support bots.
- Users can get started with Pipecat on their local machine and then move their agent processes to the cloud when ready. It also allows for the addition of a telephone number, image output, video input, and use of different LLMs.
- Pipecat provides code examples and a basic bot that greets a user when they join a real-time session. It uses Daily for real-time media transport and ElevenLabs for text-to-speech.
- For production use, Pipecat recommends using WebRTC for client-server audio, with Daily as a quick way to get started. Voice Activity Detection (VAD) is an essential component for a natural feeling conversation, and Pipecat makes use of WebRTC VAD by default.