13 Repos
Retrieving a specific message offset based on a temporal value.
Distinct from Offset-Based Addressing: Focuses on translating timestamps to offsets, whereas Offset-Based Addressing is about navigating binary blobs.
Explore 13 awesome GitHub repositories matching data & databases · Timestamp-Based Offset Lookups. Refine with filters or upvote what's useful.
Sarama is an Apache Kafka Go client library that provides native support for the Kafka protocol. It includes a protocol client for managing offsets and timestamps, a producer implementation for sending messages, and a consumer group coordinator to balance workloads across multiple instances. The library enables high throughput data streaming through concurrent message production and maintains strict partition ordering during network retries. It supports secure communication with Kafka brokers using certificate-based encryption to protect data traffic. The project covers a broad range of dist
Allows retrieving specific message offsets for given timestamps to initiate reading from precise points in time.
WWDC is a native macOS video player and conference session manager designed for streaming and organizing developer conference videos. It functions as a video transcription browser and annotation tool, allowing users to track viewing progress and organize technical sessions into personalized learning paths. The application enables navigation through videos via searchable, multi-language text transcripts. Users can create searchable reference points by annotating specific video timestamps with custom notes and distribute content by sharing session links or extracting short video clips. The sys
Links searchable text indices to specific video time offsets for instant navigation during playback.
Silero VAD is a voice activity detection model and deep learning speech classifier designed to distinguish human speech from silence across diverse languages and noisy environments. It functions as a pre-trained neural network capable of identifying speech segments within both static audio recordings and real-time data streams. The project includes a language identification tool for classifying spoken languages and a framework for fine-tuning audio models. It provides utilities for optimizing detection thresholds using validation datasets and retraining the model with custom labeled audio to
Maps model output indices to temporal offsets to isolate specific voice segments from recordings.
Autocut is a text-based video editor and automatic speech recognition tool. It allows users to cut and merge video clips by modifying a text transcript instead of using a traditional timeline. The system operates as an FFmpeg video processor and subtitle manipulation utility. It converts spoken audio into text and compacts subtitle files into simplified formats, enabling the removal of unwanted video segments by deleting corresponding sentences from a transcription file. The project covers automated video transcription, non-linear video cutting, and subtitle file management. It supports hard
Generates precise video edit points by mapping text indices from a transcript to specific timecodes.
kcat ist ein Kommandozeilen-Client für Apache Kafka, der verwendet wird, um Nachrichten unter Verwendung des nativen Wire-Protokolls zu produzieren, zu konsumieren und zu debuggen. Er bietet eine Reihe von Tools für die Interaktion mit Kafka-Clustern, einschließlich eines Protokoll-Debuggers zur Untersuchung von Cluster-Metadaten und eines Transaktionsmanagers für die Handhabung atomarer Nachrichten-Batches. Das Projekt verfügt über einen spezialisierten Avro-Schema-Dekoder, der binär kodierte Nachrichten in menschenlesbares JSON umwandelt, indem er sich in Remote-Schema-Registries oder lokale Dateien integriert. Zusätzlich enthält es einen In-Memory-Simulator, der das Testen von Producer- und Consumer-Logik durch die Simulation ephemerer Broker-Verhaltensweisen ermöglicht, ohne dass eine externe Infrastruktur erforderlich ist. Das Toolset deckt ein breites Spektrum an Messaging-Operationen ab, einschließlich Unterstützung für ausbalancierte Consumer-Gruppen, zeitstempelbasierte Offset-Suche und transaktionales Daten-Streaming von der Standardeingabe. Zudem bietet es Dienstprogramme für die Konfiguration der Verbindungssicherheit und die Untersuchung von Cluster-Metadaten.
Retrieves specific message offsets based on temporal values for targeted data recovery and analysis.
LyricsX is a macOS application that renders synchronized song lyrics over the system UI during music playback. It functions as a desktop display tool, an external lyric aggregator, and a synchronization utility. The application fetches lyrics from multiple remote data sources using current playback metadata and provides a script converter to translate text between Traditional and Simplified Chinese characters. It also includes a lyric file manager for importing and exporting common lyric formats via drag-and-drop interactions. The tool provides capabilities for timing synchronization to matc
Adjusts the temporal offset of lyric lines to align precisely with the audio playback clock.
This project is an anime scene reverse image search engine that matches a screenshot to the exact anime episode and timestamp. It is designed as a self-hosted search service that can be deployed using Docker containers and pre-indexed databases, enabling private operation on local or custom infrastructure. At its core, the system extracts visual features from frames using a convolutional neural network trained on anime imagery. Query images provided via URL are processed through the same feature extraction pipeline, and an approximate nearest neighbor search matches the query against millions
Translates matched frame numbers to exact anime episode, offset, and scene metadata.
MuJing is a contextual English vocabulary learner and interactive media player designed for language study. It extracts words from videos and documents to provide real-world examples and media clips for memorization, functioning as a subtitle-based language tool and a lemma-based word list generator. The system differentiates itself by linking vocabulary lists to specific video timestamps and subtitles for auditory and visual reinforcement. It includes a video player with bilingual subtitles and keyboard-based transcription and spelling exercises to build muscle memory through movie and telev
Maps vocabulary terms to precise video playback offsets for immediate retrieval of audiovisual examples.
KafkaJS ist ein reiner JavaScript-Client für Apache Kafka, der die notwendigen Tools bereitstellt, um Nachrichten aus einem Kafka-Cluster zu produzieren und zu konsumieren, ohne native Abhängigkeiten oder externe Addons zu erfordern. Er fungiert als umfassende Integrationsbibliothek für Node.js-Anwendungen, um an verteilter Nachrichtenverarbeitung und Echtzeit-Event-Streaming teilzunehmen. Das Projekt zeichnet sich durch seine native Implementierung des Kafka-Wire-Protokolls aus, wodurch C++-Abhängigkeiten vermieden werden. Es verfügt über einen Security-Client, der SSL-, TLS- und SASL-Authentifizierung unterstützt, sowie über transaktionale Funktionen, die atomares Nachrichten-Senden und verknüpfte Offset-Commitments ermöglichen, um Exactly-Once-Processing sicherzustellen. Die Bibliothek deckt ein breites Spektrum an operativen Bereichen ab, einschließlich vollständiger Cluster-Administration zur Verwaltung von Topics und Consumer-Groups, fortgeschrittenem Partition-Routing und Zuweisungsstrategien sowie umfassender Telemetrie durch event-gesteuertes Monitoring. Sie implementiert zudem Netzwerk-Zuverlässigkeitsmuster wie Exponential-Backoff-Retries und Rack-Aware-Data-Fetching, um die Latenz zu optimieren.
Fetches the earliest or most recent offsets for a topic based on a specific timestamp.
DashPlayer is a language learning video player designed for vocabulary and grammar study. It integrates an AI subtitle generator to create machine-translated captions and grammatical sentence analysis for video content. The project features a bilingual subtitle renderer that displays dual-language captions with toggleable visibility. It includes a remote media downloader to fetch online video content via URL and a utility to split long files into smaller segments for more manageable study sessions. The playback system supports sentence-based navigation, allowing users to jump between subtitl
Enables jumping between subtitle lines and repeating phrases using keyboard and Bluetooth inputs.
LLPlayer is a language learning media player and AI subtitle generator that integrates large language models for real-time audio transcription and translation. It functions as an LLM-integrated video player and SRT transcription tool, utilizing local or remote AI models to generate text subtitles from audio and video streams. The project distinguishes itself through a contextual translation workflow that sends preceding subtitle lines to language models to maintain conversational flow and sentence structure. It also includes an optical character recognition system to convert bitmap-based subt
Maps searchable subtitle text to specific playback timestamps for rapid video navigation.
Auto-subs is an AI transcription and automatic captioning tool that converts spoken audio from video files into synchronized subtitles. It functions as a subtitle generator and a transcription bridge, enabling the conversion of speech to text with automatic speaker identification and multi-language translation support. The software prioritizes data privacy by utilizing on-device AI inference to process audio and video files locally on the user's hardware. It distinguishes itself by offering deep integration with professional video editing workflows, allowing users to export timing and transcr
Binds specific words or phrases to precise time offsets to keep captions synchronized during edits.
danmu_api is a bullet chat API gateway that aggregates video comments from multiple platforms and serves them through a standardized interface for use in media players. It includes a metadata matching service to identify video content across platforms using keywords or file names. The system uses an adapter-based normalization process to translate diverse platform response formats into a single schema and utilizes a Redis-backed cache to store search results and comment streams. It features a processing engine that cleans comment streams using keyword and regular expression filters. The proj
Adjusts comment delivery timing using linear scaling and manual offsets to align text with video playback.