3 Repos
Integrated audio processing pipelines that handle input and output within a single model to minimize latency.
Distinct from Speech Processing: Distinct from Speech Processing: focuses on the low-latency, single-model execution architecture rather than general speech analysis.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Realtime Processing Pipelines. Refine with filters or upvote what's useful.
LiveKit is a comprehensive framework for building and orchestrating real-time, multimodal AI agents that interact with users through voice, video, and text. It provides a centralized, event-driven architecture to manage the entire lifecycle of automated participants, from initialization and session state management to graceful shutdown. By utilizing a selective forwarding unit, the platform efficiently routes media streams between participants and agents, ensuring low-latency communication and secure, token-based authentication for all connections. The platform distinguishes itself through it
Handles audio input and output within a single model to minimize latency and improve the expressiveness of conversational responses.
Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech systems. It utilizes a frame-based data pipeline to route audio, video, and text through a modular sequence of processors, enabling the orchestration of low-latency conversational AI. The project is distinguished by its ability to coordinate complex multimodal services, including speech-to-text, language models, and text-to-speech, within a single pipeline. It features semantic voice activity detection for natural turn-taking, state-machine conversation flows for dialogue manag
Utilizes integrated audio processing pipelines that handle input and output within a single model to minimize latency.
RealtimeSTT is a local speech-to-text engine and real-time automatic speech recognition server. It utilizes transformer-based recognition and omnilingual pipelines to convert live audio streams into text, providing a WebSocket-based streaming API for raw PCM audio transmission. The project is distinguished by a dual-backend transcription pipeline that uses a lightweight engine for immediate partial suggestions and a heavier model for final high-accuracy results. It includes a wake word detection system to trigger recording and employs a shared-resource inference model to distribute heavy spee
Produces fast, preliminary transcriptions using a lightweight model while a larger model processes the final result.