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17 Repos

Awesome GitHub RepositoriesMedia Processing Pipelines

Automated workflows for ingesting, processing, and transforming audio or video media for machine learning applications.

Explore 17 awesome GitHub repositories matching artificial intelligence & ml · Media Processing Pipelines. Refine with filters or upvote what's useful.

Awesome Media Processing Pipelines GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • ffmpeg/ffmpegAvatar von FFmpeg

    FFmpeg/FFmpeg

    61,176Auf GitHub ansehen↗

    FFmpeg is a cross-platform multimedia framework designed for the recording, conversion, and streaming of audio and video content. It functions as a comprehensive toolkit that provides both a command-line utility for direct media manipulation and a collection of low-level libraries for integration into custom applications. At its core, the project utilizes a packet-based stream engine and a format-agnostic abstraction layer to handle diverse media standards, containers, and network protocols. The framework distinguishes itself through a modular, graph-based filter execution model that allows f

    Processes decoded audio and video frames through custom pipelines to perform tasks like resizing, deinterlacing, or mixing before final encoding.

    Caudiocffmpeg
    Auf GitHub ansehen↗61,176
  • deepfakes/faceswapAvatar von deepfakes

    deepfakes/faceswap

    55,289Auf GitHub ansehen↗

    Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users to map facial identities between source and destination datasets while maintaining structural alignment and lighting consistency across video frames. The project distinguishes itself through a highly extensible plugin-based architecture that handles hardware-accelerated process

    Coordinates automated workflows for ingesting, processing, and transforming video media for facial synthesis applications.

    Pythondeep-face-swapdeep-learningdeep-neural-networks
    Auf GitHub ansehen↗55,289
  • google/mediapipeAvatar von google

    google/mediapipe

    35,673Auf GitHub ansehen↗

    MediaPipe is a cross-platform machine learning framework designed for building and deploying pipelines that process live and streaming media. It provides a system for connecting processing components into custom machine learning chains to analyze real-time audio and video streams. The framework includes a suite of pre-trained models for tasks such as hand, face, and pose tracking, along with tools for retraining and customizing these models with specific datasets. It also features a dedicated benchmarker for measuring the execution speed and accuracy of machine learning models directly within

    Allows the creation of specialized media processing pipelines by connecting series of processing steps for real-time analysis.

    C++
    Auf GitHub ansehen↗35,673
  • google/exoplayerAvatar von google

    google/ExoPlayer

    21,918Auf GitHub ansehen↗

    ExoPlayer is an Android media player library and framework designed for playing audio and video content on Android devices. It serves as an adaptive streaming player capable of handling dynamic bitrate switching for streaming protocols such as DASH and HLS. The library provides a foundation for building custom media players with unique playback controls and specialized media source handling. It supports digital content delivery by enabling the streaming of high-quality video over varying network conditions through automatic quality level switching. The framework covers core media playback ca

    Constructs playback logic by linking modular components for loading, buffering, and rendering media data.

    Java
    Auf GitHub ansehen↗21,918
  • drewthomasson/ebook2audiobookAvatar von DrewThomasson

    DrewThomasson/ebook2audiobook

    19,291Auf GitHub ansehen↗

    This project is a scalable, containerized pipeline designed to transform digital documents and image-based ebooks into narrated audiobooks. It functions as an end-to-end production platform that integrates text-to-speech synthesis, optical character recognition, and automated workflow management to convert various file formats into spoken audio. The system distinguishes itself through advanced linguistic analysis and voice synthesis capabilities, including the ability to identify characters within a text and assign them distinct voice profiles for multi-speaker narration. Users can further pe

    Provides a scalable architecture that packages conversion services into isolated environments to manage resource-intensive audio rendering tasks.

    Pythonaudiobookaudiobookschinese
    Auf GitHub ansehen↗19,291
  • k4yt3x/video2xAvatar von k4yt3x

    k4yt3x/video2x

    18,754Auf GitHub ansehen↗

    Video2x is a modular processing framework designed for AI-enhanced video upscaling and frame rate conversion. It functions as a comprehensive toolset for increasing the resolution and visual clarity of media files while generating intermediate frames to improve motion smoothness. The system is built to handle intensive media transformation tasks by leveraging hardware acceleration and custom encoding pipelines. The project distinguishes itself through a plugin-based architecture that allows for the integration of custom machine learning models and specialized algorithms. It utilizes a modular

    Provides a modular framework for integrating custom machine learning models and encoding configurations into automated media processing workflows.

    C++anime4kframe-interpolationmachine-learning
    Auf GitHub ansehen↗18,754
  • kkroening/ffmpeg-pythonAvatar von kkroening

    kkroening/ffmpeg-python

    10,999Auf GitHub ansehen↗

    ffmpeg-python is a Python wrapper that translates programmatic method calls into command-line arguments for executing FFmpeg media processing tasks. It functions as a multimedia transcoding interface and a media stream capture tool, allowing for the recording of live audio and video from hardware devices and network sources. The library features a fluent interface for constructing complex directed graphs of audio and video filters through method chaining. It also includes an FFprobe metadata extractor that retrieves structured technical properties from media files and returns them as Python d

    Applies independent filters to separate video and audio streams before concatenating them into a single file.

    Python
    Auf GitHub ansehen↗10,999
  • aws/chaliceAvatar von aws

    aws/chalice

    11,062Auf GitHub ansehen↗

    Chalice is a framework for building and deploying serverless applications and REST APIs on AWS Lambda using Python. It functions as an infrastructure-as-code generator, mapping application logic and routing definitions directly to cloud compute resources while automating the provisioning and management of the underlying environment. The framework distinguishes itself by analyzing source code to automatically construct the minimum necessary security permissions, ensuring least-privilege access for all deployed functions. It supports modular development through blueprint-based organization and

    Analyzes uploaded media assets using automated pipelines to extract metadata for application use.

    Pythonawsaws-apigatewayaws-lambda
    Auf GitHub ansehen↗11,062
  • livekit/agentsAvatar von livekit

    livekit/agents

    9,379Auf GitHub ansehen↗

    This project is a framework for developing multimodal AI agents that function as programmable participants in real-time communication rooms. It enables the construction of agents that can see, hear, and speak by integrating speech-to-text, large language models, and text-to-speech pipelines to facilitate low-latency, natural conversations. The system is distinguished by its advanced orchestration of real-time media and conversational flow, including support for full-duplex speech, preemptive response generation, and sophisticated interruption management. It further differentiates itself throu

    Implements automated workflows that sequentially process speech recognition, language modeling, and synthesis nodes.

    Pythonagentsaiopenai
    Auf GitHub ansehen↗9,379
  • bytedeco/javacvAvatar von bytedeco

    bytedeco/javacv

    8,310Auf GitHub ansehen↗

    JavaCV provides a Java-based interface for native computer vision and video processing libraries. It functions as a wrapper for native vision libraries, allowing Java applications to perform image analysis, object detection, and video stream processing. The project integrates comprehensive computer vision capabilities, including facial recognition, image segmentation, and optical flow analysis for motion tracking. It also provides tools for hardware geometry calibration and projector-camera alignment to ensure accurate spatial representation. The system covers high-performance media renderin

    Provides a library of filters for frame-level transformations such as fading, overlaying, and color changes.

    Javacomputer-visionffmpegjava
    Auf GitHub ansehen↗8,310
  • yaoapp/yaoAvatar von YaoApp

    YaoApp/yao

    7,544Auf GitHub ansehen↗

    Yao is an LLM agent framework and low-code web app builder designed for orchestrating autonomous AI agents. It provides a platform to design, deploy, and coordinate agents with specialized personas that can plan tasks, utilize external tools, and execute multi-stage pipelines. The project distinguishes itself through a Model Context Protocol server for connecting assistants to external binaries and HTTP services, and a gRPC remote execution engine that allows agents to manage remote servers and devices. It includes a model-agnostic provider bridge that supports dynamic switching between vario

    Allows replacing default task agents with custom versions to modify execution behavior within AI pipelines.

    Goagentagentic-aiagents
    Auf GitHub ansehen↗7,544
  • huibq/keep-aliveAvatar von Huibq

    Huibq/keep-alive

    6,307Auf GitHub ansehen↗

    Keep Alive is a cross-platform music streaming framework that aggregates free online music from multiple source providers through an extensible plugin system. It is designed to stream high-quality audio up to 320kbps across desktop and mobile applications by loading external plugins that handle source fetching and playback control. The project distinguishes itself with a plugin-based architecture that decouples music source fetching from the player, allowing third-party music players to integrate and access tracks through interchangeable source links. A dynamic plugin registry enables runtime

    Implements an event-driven playback pipeline that routes music through handlers for buffering, decoding, and state management.

    JavaScript
    Auf GitHub ansehen↗6,307
  • accord-net/frameworkAvatar von accord-net

    accord-net/framework

    4,540Auf GitHub ansehen↗

    Dieses Projekt ist ein Framework für wissenschaftliches Rechnen im .NET-Ökosystem und bietet eine umfassende Suite von Bibliotheken für numerische Analyse, Statistik und mathematische Optimierung. Es dient als grundlegendes Toolkit für die Entwicklung von Anwendungen in den Bereichen Machine Learning, digitale Signalverarbeitung und Computer Vision. Das Framework bietet spezialisierte Toolkits für das Training und die Bereitstellung prädiktiver Modelle, einschließlich neuronaler Netze, Support Vector Machines und Entscheidungsbäumen. Es zeichnet sich zudem durch tiefe Integrationen für Echtzeit-Bildanalyse aus, wie etwa Objektverfolgung und Gesichtserkennung, ergänzt durch eine dedizierte Bibliothek für digitale Signalverarbeitung zur Erfassung und Filterung von Audio- und Sensorsignalen. Das Funktionsspektrum erstreckt sich auf hochgradige Matrixzerlegung und lineare Algebra, probabilistische Zustandsmodellierung und heuristische Suchalgorithmen. Es deckt zudem eine breite Palette an Datenmanipulations-Dienstprogrammen ab, von Dimensionsreduktion und Normalisierung bis hin zur Organisation räumlicher Daten und Komponenten für wissenschaftliche Visualisierung. Das System enthält Hardware-Integrationscontroller für Kamerakonfiguration, GPIO-Port-Management und spezialisierte Tiefensensor-Hardware.

    Enumerates and selects video filters to configure the modular media processing pipeline.

    C#
    Auf GitHub ansehen↗4,540
  • modstart-lib/aigcpanelAvatar von modstart-lib

    modstart-lib/aigcpanel

    4,576Auf GitHub ansehen↗

    Aigcpanel is a visual workflow automation tool and model lifecycle manager designed for generative AI media pipelines. It provides a unified interface to install, launch, and configure both local and remote AI model endpoints, acting as an orchestration platform for large language models and AI tools. The system features a drag-and-drop node editor for chaining AI models and scripts into automated processing pipelines. It distinguishes itself with a breakpoint-aware execution model that allows users to pause and resume long media tasks from specific points in the workflow. Additionally, it in

    Provides automated workflows for performing speech-to-text transcription and background removal on media files.

    TypeScriptaiaigccosyvoice
    Auf GitHub ansehen↗4,576
  • brianchirls/seriously.jsAvatar von brianchirls

    brianchirls/Seriously.js

    3,887Auf GitHub ansehen↗

    Seriously.js is a WebGL video processing framework and browser-based graphics engine designed for real-time video composition and image manipulation. It functions as a node-based visual compositor that organizes media sources and effects into a directed acyclic graph, using a custom fragment shader compiler to transform these graphs into optimized GLSL shaders for GPU-accelerated rendering. The engine features a modular web media pipeline and a plugin-based architecture, allowing for the integration of custom effects, input sources, and output targets. It enables the development of interactiv

    Ships a modular media pipeline for applying frame-level transformations and effects to ingested web media.

    JavaScript
    Auf GitHub ansehen↗3,887
  • steipete/summarizeAvatar von steipete

    steipete/summarize

    3,771Auf GitHub ansehen↗

    Summarize is a command line tool and multimodal content extractor designed to generate concise summaries from web pages, documents, and media files. It functions as an orchestrator that connects developer tools to various language model providers to process and condense information. The system provides specialized capabilities for audio and video processing, including transcription with speaker identification and the extraction of timestamped visual markers from video slides. It also includes a translation utility to convert generated summaries and extracted text into different target languag

    Sequences transcription, OCR, and diarization to transform raw multimedia files into clean text.

    TypeScriptaiclisummarize
    Auf GitHub ansehen↗3,771
  • gpac/gpacAvatar von gpac

    gpac/gpac

    3,205Auf GitHub ansehen↗

    GPAC is an open-source multimedia framework built around a pluggable filter graph pipeline, where modular processing units called filters connect into a directed graph to handle media workflows. At its core, the framework centers all media packaging and manipulation on the ISO Base Media File Format (ISOBMFF), with specialized tools for reading, writing, fragmenting, and encrypting MP4 and related containers. It also provides a declarative scene graph composition system for describing interactive multimedia scenes using MPEG-4 BIFS, X3D, SVG, or VRML syntax, alongside a hardware-accelerated re

    Assembles sequences of processing blocks to create arbitrary media workflows.

    Catsc3broadcastcenc
    Auf GitHub ansehen↗3,205
  1. Home
  2. Artificial Intelligence & ML
  3. Machine Learning
  4. Infrastructure
  5. Domain-Specific Processing Pipelines
  6. Media Processing Pipelines

Unter-Tags erkunden

  • Agent Workflow CustomizersModular logic used to override and modify inputs and outputs within AI agent processing pipelines. **Distinct from Media Processing Pipelines:** Focuses on the agent's logic nodes rather than general ML data transformation pipelines
  • Media Filter Pipelines1 Sub-TagModular pipelines for applying transformations like resizing, deinterlacing, or mixing to audio and video frames. **Distinct from Media Processing Pipelines:** Distinct from general media processing pipelines: focuses on the graph-based filter execution model for frame-level transformations.