7 repository-uri
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 este o stație de lucru audio digitală (DAW), mixer audio multitrack și sequencer MIDI. Funcționează ca un editor audio non-liniar și un host de plugin-uri pentru rularea efectelor și instrumentelor terțe. Sistemul oferă capabilități specializate pentru post-producția audio prin sincronizarea cadrelor video, precum și secvențierea pentru performanțe live pentru declanșarea clipurilor și modelelor în timp real. De asemenea, suportă mixarea tactilă prin maparea suprafețelor de control și configurarea controllerelor hardware. Software-ul acoperă o gamă largă de nevoi de producție audio, inclusiv înregistrare multitrack, secvențiere și compoziție MIDI, mixare profesională și export audio multicanal. Framework-ul său de procesare include suport pentru plugin-uri standard în industrie și un sistem de rutare a semnalului de tip matrice.
Provides precise temporal alignment of audio segments with corresponding video frames for post-production scoring.
VITS-fast-fine-tuning este un pipeline pentru adaptarea modelelor de sinteză vocală la voci țintă specifice folosind seturi mici de date audio. Funcționează ca un instrument de adaptare rapidă a vorbitorului și un sintetizator vocal multilingv capabil să genereze audio vorbit în diferite limbi. Sistemul oferă un framework pentru conversia vocală many-to-many, transformând identitatea unui vorbitor în alta, păstrând în același timp conținutul lingvistic original. Permite adaptarea unei voci pentru text-to-speech prin fine-tuning-ul unui model pre-antrenat cu clipuri audio sau surse video. Proiectul acoperă sinteza vocală end-to-end și procesarea audio, utilizând generarea de forme de undă adversariale și căutarea alinierii monotonice pentru a produce audio de înaltă fidelitate. Încorporează un predictor de durată stocastic pentru a gestiona variațiile în ritmul vorbirii și suportă transferul de modele pre-antrenate.
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.