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3 Repos

Awesome GitHub RepositoriesRealtime Processing Pipelines

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.

Awesome Realtime Processing Pipelines GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • livekit/livekitAvatar von livekit

    livekit/livekit

    19,358Auf GitHub ansehen↗

    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.

    Gogolangmedia-serversfu
    Auf GitHub ansehen↗19,358
  • pipecat-ai/pipecatAvatar von pipecat-ai

    pipecat-ai/pipecat

    12,846Auf GitHub ansehen↗

    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.

    Pythonaichatbot-frameworkchatbots
    Auf GitHub ansehen↗12,846
  • koljab/realtimesttAvatar von KoljaB

    KoljaB/RealtimeSTT

    9,477Auf GitHub ansehen↗

    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.

    Pythonpythonrealtimespeech-to-text
    Auf GitHub ansehen↗9,477
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  2. Artificial Intelligence & ML
  3. Machine Learning
  4. Speech Processing
  5. Realtime Processing Pipelines

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  • Dual-Model Transcription PipelinesSystems using a combination of lightweight and heavy models to balance latency and accuracy. **Distinct from Realtime Processing Pipelines:** Specifically implements the pattern of using preliminary fast models and subsequent high-accuracy models for the same stream.