7 dépôts
Processes individual video frames to ensure precise temporal synchronization with corresponding audio segments.
Distinct from Frame Extractors: Focuses on the temporal alignment of frames to audio, rather than just sampling or extracting frames.
Explore 7 awesome GitHub repositories matching graphics & multimedia · Temporal Frame Alignment. Refine with filters or upvote what's useful.
Wav2Lip is a deep learning lip sync model and neural talking head framework designed to synchronize the lip movements in a video to match a provided audio file. It functions as a computer vision lip synchronizer and speech-to-lip generator that maps speech patterns to visual mouth movements to produce realistic talking head videos. The system utilizes a framework for training and evaluating models that align audio and video frames. This includes the ability to train lip-sync models and visual discriminators using speech-to-lip datasets and evaluating the resulting synchronization accuracy thr
Processes video sequences as individual frames to ensure perfect alignment with corresponding audio slices.
This project is an end-to-end text-to-speech engine and deep learning voice synthesizer. It functions as a neural speech synthesis framework that converts written text directly into audio waveforms using a single neural network. The system implements an adversarial framework and a conditional variational autoencoder to generate high-fidelity artificial speech. It utilizes a generative adversarial network to ensure synthesized audio is indistinguishable from real human speech. The toolkit provides capabilities for neural speech synthesis, text-to-audio generation, and the training of custom v
Automatically learns the alignment and duration between text characters and audio frames without external tools.
Dejavu is a Python audio fingerprinting library and recognition engine. It functions as a digital audio signature tool used to analyze sound waves and create unique identifiers for the purposes of audio search and retrieval. The project enables automatic music identification by matching live audio feeds or recorded clips against a database of fingerprints. It covers audio content matching and digital audio archiving to identify original source recordings from a stored collection. The system incorporates capabilities for generating audio fingerprints, identifying audio tracks, and recognizing
Validates candidate matches by ensuring the temporal distance between fingerprints is consistent across the recording.
Ardour est une station de travail audio numérique (DAW), un mixeur audio multipiste et un séquenceur MIDI. Il fonctionne comme un éditeur audio non linéaire et un hôte de plugins pour exécuter des effets et instruments tiers. Le système fournit des capacités spécialisées pour le mixage audio de post-production via la synchronisation d'images vidéo, ainsi que le séquençage de performance live pour déclencher des clips et des patterns en temps réel. Il prend également en charge le mixage tactile via le mapping de surfaces de contrôle et la configuration de contrôleurs matériels. Le logiciel couvre un large éventail de besoins en production audio, incluant l'enregistrement multipiste, le séquençage et la composition MIDI, le mixage professionnel et l'exportation audio multicanal. Son framework de traitement inclut un support de plugins standard de l'industrie et un système de routage de signal de type matrice.
Provides precise temporal alignment of audio segments with corresponding video frames for post-production scoring.
VITS-fast-fine-tuning est un pipeline pour adapter des modèles de synthèse vocale à des voix cibles spécifiques en utilisant de petits jeux de données audio. Il fonctionne comme un outil d'adaptation rapide du locuteur et un synthétiseur vocal multilingue capable de générer de l'audio parlé à travers différentes langues. Le système fournit un framework pour la conversion de voix many-to-many, transformant l'identité d'un locuteur en un autre tout en préservant le contenu linguistique original. Il permet l'adaptation d'une voix pour la synthèse texte-parole en affinant un modèle pré-entraîné avec des clips audio ou des sources vidéo. Le projet couvre la synthèse vocale end-to-end et le traitement audio, utilisant la génération de forme d'onde adverse et la recherche d'alignement monotone pour produire un audio haute fidélité. Il incorpore un prédicteur de durée stochastique pour gérer les variations dans le rythme de parole et prend en charge le transfert de modèle pré-entraîné.
Automatically learns the mapping between text characters and audio frames during the training process.
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
Compares key-frame intervals and sync sample positions across files to detect misalignment before DASH packaging.
Intro Skipper is a media server plugin and automated playback utility designed to identify and bypass television opening sequences. It functions as an automated content sequence skipper that detects repeated introduction segments in video files to improve viewing efficiency. The tool employs audio fingerprinting to analyze audio patterns during playback, comparing waveforms against known templates to trigger skip events. It allows for the management of playback preferences across multiple client devices to determine how these opening sequences are handled. The project covers automated media
Analyzes time-stamped audio data to determine precise skip intervals for media files.