7 个仓库
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 是一个数字音频工作站(DAW)、多轨音频混音器和 MIDI 音序器。它作为一个非线性音频编辑器和用于运行第三方效果器和乐器的插件宿主。 该系统为后期制作音频配乐提供了专门的功能,包括视频帧同步,以及用于实时触发片段和模式的现场表演音序功能。它还支持通过控制界面映射和硬件控制器配置进行触控混音。 该软件涵盖了广泛的音频制作需求,包括多轨录音、MIDI 音序与编曲、专业混音以及多声道音频导出。其处理框架包括行业标准的插件支持和矩阵式信号路由系统。
Provides precise temporal alignment of audio segments with corresponding video frames for post-production scoring.
VITS-fast-fine-tuning 是一个使用小型音频数据集将语音合成模型适配到特定目标音色的流水线。它充当快速说话人适配工具和多语言语音合成器,能够生成跨不同语言的口语音频。 该系统提供了一个用于多对多语音转换的框架,在保留原始语言内容的同时转换说话人的身份。它允许通过使用音频片段或视频源微调预训练模型来适配文本转语音的音色。 该项目涵盖端到端语音合成和音频处理,利用对抗性波形生成和单调对齐搜索来产生高保真音频。它结合了随机持续时间预测器来管理说话节奏的变化,并支持预训练模型迁移。
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.