Search Results for "learning vector quantization"

whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples. whisper.cpp supports integer quantization of the Whisper ggml models. Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently.

Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP,...

...QuestDB achieves high performance from a column-oriented storage model, massively-parallelized vector execution, SIMD instructions, and various low-latency techniques. The entire codebase was built from the ground up in Java and C++, with no dependencies, and is 100% free from garbage collection. We provide a live demo provisioned with the latest QuestDB release and sample datasets.

Implementation of the algorithm of the paper: "Fractal image compression based on Delaunay triangulation and vector quantization" It is a Fractal compression algorithm, based on relationships between regions of the image (in this case triangles), rather than in explicitly the pixel values themselves. Video demo: https://frojasg1.com:8443/resource_counter/resourceCounter?operation=countAndForward&url=https%3A%2F%2Fsummer-heart-0930.chufeiyun1688.workers.dev%3A443%2Fhttps%2Ffrojasg1.com%2Fdemos%2Faplicaciones%2FfractalCompressor%2Ffractal_image_compression_v1.0_demo.mp4%3Forigin%3Dsourceforge&origin=web

GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space. The links provided contain word vectors obtained from the respective corpora.