7 Repos
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 ist eine digitale Audio-Workstation, ein Multitrack-Audio-Mixer und ein MIDI-Sequenzer. Es fungiert als nicht-linearer Audio-Editor und Plugin-Host für die Ausführung von Effekten und Instrumenten von Drittanbietern. Das System bietet spezialisierte Funktionen für die Audio-Nachbearbeitung durch Videosynchronisation sowie für Live-Performance-Sequenzierung zum Triggern von Clips und Patterns in Echtzeit. Es unterstützt zudem taktiles Mixing durch Control-Surface-Mapping und die Konfiguration von Hardware-Controllern. Die Software deckt eine breite Palette an Anforderungen der Audioproduktion ab, einschließlich Multitrack-Aufnahme, MIDI-Sequenzierung und Komposition, professionellem Mixing und Mehrkanal-Audio-Export. Sein Verarbeitungs-Framework umfasst branchenüblichen Plugin-Support und ein Matrix-basiertes Signal-Routing-System.
Provides precise temporal alignment of audio segments with corresponding video frames for post-production scoring.
VITS-fast-fine-tuning is a pipeline for adapting speech synthesis models to specific target voices using small audio datasets. It functions as a fast speaker adaptation tool and a multilingual speech synthesizer capable of generating spoken audio across different languages. The system provides a framework for many-to-many voice conversion, transforming the identity of one speaker into another while preserving the original linguistic content. It allows for the adaptation of a voice for text-to-speech by fine-tuning a pre-trained model with audio clips or video sources. The project covers end-
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.